Add application file

.env

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+GEMINI_API_KEY=AIzaSyAOK9vRTSRQzd22B2gmbiuIePbZTDyaGYs
+RAPIDAPI_KEY=a9712241damsh9d248dc7bd8afabp171beajsn5d30f6e126b7

app.py

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+import streamlit as st
+import http.client
+import json
+import os
+import PyPDF2
+import io
+import requests
+import time
+from google import genai
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+# ----------------------------
+# RAG Chatbot Implementation
+# ----------------------------
+from sentence_transformers import SentenceTransformer
+import faiss
+import numpy as np
+
+class SimpleRAG:
+    def __init__(self, api_key):
+        # Initialize the embedding model and generative AI client
+        self.embedder = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+        self.client = genai.Client(api_key=api_key)
+        self.model = "gemini-2.0-flash"
+        self.index = None
+        self.chunks = []
+        self.is_initialized = False
+        self.processing_status = None
+
+    def chunk_text(self, text, chunk_size=700):
+        """Split text into smaller chunks."""
+        words = text.split()
+        return [' '.join(words[i:i + chunk_size]) for i in range(0, len(words), chunk_size)]
+
+    def process_search_data(self, search_data):
+        """
+        Process job search result data and index it.
+        For each job posting, extract only the 'job_title' and 'job_description' fields.
+        """
+        try:
+            self.processing_status = "Processing job search data..."
+            job_docs = []
+            for job in search_data:
+                title = job.get('job_title', '')
+                description = job.get('job_description', '')
+                # Create a document string with only job title and description.
+                doc = f"Job Title: {title}. Job Description: {description}."
+                job_docs.append(doc)
+            # Join each job document with a delimiter.
+            combined_text = " ||| ".join(job_docs)
+            if not combined_text.strip():
+                raise Exception("No text found in job search results.")
+            self.chunks = self.chunk_text(combined_text)
+            if not self.chunks:
+                raise Exception("No content chunks were generated from job data.")
+            embeddings = self.embedder.encode(self.chunks)
+            vector_dimension = embeddings.shape[1]
+            self.index = faiss.IndexFlatL2(vector_dimension)
+            self.index.add(np.array(embeddings).astype('float32'))
+            self.is_initialized = True
+            self.processing_status = f"RAG system initialized with {len(self.chunks)} chunks."
+            return {"status": "success", "message": self.processing_status}
+        except Exception as e:
+            self.processing_status = f"Error: {str(e)}"
+            self.is_initialized = False
+            return {"status": "error", "message": str(e)}
+
+    def get_status(self):
+        """Return current processing status."""
+        return {
+            "is_initialized": self.is_initialized,
+            "status": self.processing_status
+        }
+
+    def get_relevant_chunks(self, query, k=3):
+        """Retrieve top-k relevant text chunks for a query."""
+        query_vector = self.embedder.encode([query])
+        distances, chunk_indices = self.index.search(query_vector.astype('float32'), k)
+        return [self.chunks[i] for i in chunk_indices[0]]
+
+    def query(self, question):
+        """Query the RAG system with a question."""
+        if not self.is_initialized:
+            raise Exception("RAG system not initialized. Please process job data first.")
+        try:
+            context = self.get_relevant_chunks(question)
+            prompt = f"""
+            Based on the following context, provide a clear and concise answer.
+            If the context doesn't contain enough relevant information, say "I don't have enough information to answer that question."
+
+            Context:
+            {' '.join(context)}
+
+            Question: {question}
+            """
+            response = self.client.models.generate_content(model=self.model, contents=prompt)
+            return {
+                "status": "success",
+                "answer": response.text.strip(),
+                "context": context
+            }
+        except Exception as e:
+            return {
+                "status": "error",
+                "message": str(e)
+            }
+
+# ----------------------------
+# Main Job Search Engine Code
+# ----------------------------
+# Configure page
+st.set_page_config(page_title="AI Job Finder", page_icon="💼", layout="wide")
+
+# Styling
+st.markdown("""
+<style>
+    .main-header {
+        font-size: 2.5rem;
+        color: #4169E1;
+    }
+    .sub-header {
+        font-size: 1.5rem;
+        color: #6C757D;
+    }
+    .success-message {
+        background-color: #D4EDDA;
+        color: #155724;
+        padding: 10px;
+        border-radius: 5px;
+        margin-bottom: 20px;
+    }
+    .info-box {
+        background-color: #E7F3FE;
+        border-left: 6px solid #2196F3;
+        padding: 10px;
+        margin-bottom: 15px;
+    }
+    .search-options {
+        margin-top: 20px;
+        margin-bottom: 20px;
+    }
+    /* Chatbot styling */
+    .chat-box {
+        background-color: #F8F9FA;
+        border-radius: 10px;
+        padding: 20px;
+        margin-bottom: 20px;
+    }
+    .user-message {
+        color: #0D6EFD;
+        font-weight: bold;
+    }
+    .bot-message {
+        color: #198754;
+        font-weight: bold;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# Header
+st.markdown('<p class="main-header">AI-Powered Job Finder</p>', unsafe_allow_html=True)
+st.markdown('<p class="sub-header">Upload your resume and find relevant jobs</p>', unsafe_allow_html=True)
+
+# Initialize session state variables
+if 'resume_text' not in st.session_state:
+    st.session_state.resume_text = ""
+if 'resume_parsed' not in st.session_state:
+    st.session_state.resume_parsed = False
+if 'parsed_data' not in st.session_state:
+    st.session_state.parsed_data = {}
+if 'job_results' not in st.session_state:
+    st.session_state.job_results = []
+if 'search_completed' not in st.session_state:
+    st.session_state.search_completed = False
+
+# Define the JSON schema for resume parsing
+RESUME_SCHEMA = {
+    "schema": {
+        "basic_info": {
+            "name": "string",
+            "email": "string",
+            "phone": "string",
+            "location": "string"
+        },
+        "professional_summary": "string",
+        "skills": ["string"],
+        "technical_skills": ["string"],
+        "soft_skills": ["string"],
+        "experience": [{
+            "job_title": "string",
+            "company": "string",
+            "duration": "string",
+            "description": "string"
+        }],
+        "education": [{
+            "degree": "string",
+            "institution": "string",
+            "year": "string"
+        }],
+        "certifications": ["string"],
+        "years_of_experience": "number"
+    }
+}
+
+# Function to extract text from PDF
+def extract_text_from_pdf(pdf_file):
+    pdf_reader = PyPDF2.PdfReader(pdf_file)
+    text = ""
+    for page_num in range(len(pdf_reader.pages)):
+        text += pdf_reader.pages[page_num].extract_text()
+    return text
+
+# Function to parse resume with Gemini
+def parse_resume_with_gemini(resume_text):
+    try:
+        client = genai.Client(api_key=os.getenv('GEMINI_API_KEY'))
+        prompt = f"""
+        Parse the following resume text and extract information according to this exact JSON schema:
+        
+        {json.dumps(RESUME_SCHEMA, indent=2)}
+        
+        Resume text:
+        {resume_text}
+        
+        Make sure to follow the schema exactly. If any information is not available, use empty strings or empty arrays as appropriate.
+        Return ONLY the JSON object with no additional text.
+        """
+        response = client.models.generate_content(model="gemini-2.0-flash", contents=prompt)
+        try:
+            parsed_data = json.loads(response.text)
+            return parsed_data
+        except json.JSONDecodeError:
+            import re
+            json_match = re.search(r'```json\n(.*?)\n```', response.text, re.DOTALL)
+            if json_match:
+                return json.loads(json_match.group(1))
+            else:
+                st.error("Could not parse the response as JSON")
+                return RESUME_SCHEMA["schema"]
+    except Exception as e:
+        st.error(f"Error parsing resume: {str(e)}")
+        return RESUME_SCHEMA["schema"]
+
+# Function to search for jobs
+def search_jobs(query, location="", page=1):
+    try:
+        conn = http.client.HTTPSConnection("jsearch.p.rapidapi.com")
+        search_query = query.replace(" ", "%20")
+        if location:
+            search_query += f"%20in%20{location.replace(' ', '%20')}"
+        headers = {
+            'X-RapidAPI-Key': os.getenv('RAPIDAPI_KEY'),
+            'X-RapidAPI-Host': "jsearch.p.rapidapi.com"
+        }
+        conn.request("GET", f"/search?query={search_query}&page={page}&num_pages=1", headers=headers)
+        res = conn.getresponse()
+        data = res.read()
+        return json.loads(data.decode("utf-8"))
+    except Exception as e:
+        st.error(f"Error searching for jobs: {str(e)}")
+        return {"data": []}
+
+if 'filter_remote_only' not in st.session_state:
+    st.session_state.filter_remote_only = False
+if 'filter_employment_types' not in st.session_state:
+    st.session_state.filter_employment_types = []
+if 'filter_date_posted' not in st.session_state:
+    st.session_state.filter_date_posted = 0
+if 'min_salary' not in st.session_state:
+    st.session_state.min_salary = 0
+if 'max_salary' not in st.session_state:
+    st.session_state.max_salary = 1000000
+if 'filter_company_types' not in st.session_state:
+    st.session_state.filter_company_types = []
+
+# Function to apply filters to job results
+def apply_filters(jobs):
+    filtered_jobs = []
+    for job in jobs:
+        if st.session_state.filter_remote_only and not job.get('job_is_remote', False):
+            continue
+        if st.session_state.filter_employment_types and job.get('job_employment_type') not in st.session_state.filter_employment_types:
+            continue
+        if st.session_state.filter_date_posted > 0:
+            current_time = int(time.time())
+            posted_time = job.get('job_posted_at_timestamp', 0)
+            days_ago = (current_time - posted_time) / (60 * 60 * 24)
+            if days_ago > st.session_state.filter_date_posted:
+                continue
+        if job.get('job_min_salary') is not None and job.get('job_min_salary') < st.session_state.min_salary:
+            continue
+        if job.get('job_max_salary') is not None and job.get('job_max_salary') > st.session_state.max_salary:
+            continue
+        if st.session_state.filter_company_types and job.get('employer_company_type') not in st.session_state.filter_company_types:
+            continue
+        filtered_jobs.append(job)
+    return filtered_jobs
+
+# ----------------------------
+# Step 1: Resume Upload Section
+# ----------------------------
+st.subheader("Step 1: Upload Your Resume First")
+uploaded_file = st.file_uploader("Upload your resume (PDF format)", type=['pdf'])
+if uploaded_file is not None:
+    with st.spinner('Processing your resume...'):
+        resume_text = extract_text_from_pdf(uploaded_file)
+        st.session_state.resume_text = resume_text
+        parsed_data = parse_resume_with_gemini(resume_text)
+        st.session_state.parsed_data = parsed_data
+        st.session_state.resume_parsed = True
+        with st.expander("Resume Parsed Information", expanded=True):
+            col1, col2 = st.columns(2)
+            with col1:
+                st.markdown("### Basic Information")
+                basic_info = parsed_data.get("basic_info", {})
+                st.write(f"**Name:** {basic_info.get('name', 'Not found')}")
+                st.write(f"**Email:** {basic_info.get('email', 'Not found')}")
+                st.write(f"**Phone:** {basic_info.get('phone', 'Not found')}")
+                st.write(f"**Location:** {basic_info.get('location', 'Not found')}")
+                st.markdown("### Experience")
+                for exp in parsed_data.get("experience", []):
+                    st.markdown(f"**{exp.get('job_title', 'Role')} at {exp.get('company', 'Company')}**")
+                    st.write(f"*{exp.get('duration', 'Duration not specified')}*")
+                    st.write(exp.get('description', 'No description available'))
+                    st.write("---")
+            with col2:
+                st.markdown("### Skills")
+                st.write("**Technical Skills:**")
+                tech_skills = parsed_data.get("technical_skills", [])
+                st.write(", ".join(tech_skills) if tech_skills else "No technical skills found")
+                st.write("**Soft Skills:**")
+                soft_skills = parsed_data.get("soft_skills", [])
+                st.write(", ".join(soft_skills) if soft_skills else "No soft skills found")
+                st.write("**General Skills:**")
+                skills = parsed_data.get("skills", [])
+                st.write(", ".join(skills) if skills else "No general skills found")
+                st.markdown("### Education")
+                for edu in parsed_data.get("education", []):
+                    st.write(f"**{edu.get('degree', 'Degree')}** - {edu.get('institution', 'Institution')}")
+                    st.write(f"*{edu.get('year', 'Year not specified')}*")
+                st.write(f"**Years of Experience:** {parsed_data.get('years_of_experience', 'Not specified')}")
+
+st.markdown("---")
+# ----------------------------
+# Step 2: Job Search Section
+# ----------------------------
+st.subheader("Step 2: Search for Jobs")
+search_query = st.text_input("Enter your job search query (e.g., 'Python Developer')")
+location = st.text_input("Location (e.g., 'New York', 'Remote')")
+st.sidebar.markdown("### Filter Options")
+st.sidebar.checkbox("Remote Only", key="filter_remote_only")
+employment_types = ["FULLTIME", "PARTTIME", "CONTRACTOR", "INTERN"]
+st.sidebar.multiselect("Employment Type", employment_types, default=None, key="filter_employment_types")
+date_options = {"Any time": 0, "Past 24 hours": 1, "Past week": 7, "Past month": 30}
+selected_date = st.sidebar.selectbox("Date Posted", options=list(date_options.keys()), index=0)
+st.session_state.filter_date_posted = date_options[selected_date]
+st.sidebar.markdown("### Salary Range")
+col1, col2 = st.sidebar.columns(2)
+with col1:
+    st.number_input("Min ($)", value=0, step=10000, key="min_salary")
+with col2:
+    st.number_input("Max ($)", value=1000000, step=10000, key="max_salary")
+company_types = ["Public", "Private", "Nonprofit", "Government", "Startup", "Other"]
+st.sidebar.multiselect("Company Type", company_types, default=None, key="filter_company_types")
+
+if st.button("Search Jobs"):
+    if search_query:
+        with st.spinner('Searching for jobs...'):
+            final_query = search_query
+            job_results = search_jobs(final_query, location)
+            st.session_state.job_results = job_results.get('data', [])
+            st.session_state.search_completed = True
+    else:
+        st.warning("Please enter a search query")
+
+# Display Job Search Results
+if st.session_state.search_completed:
+    st.markdown("---")
+    st.subheader("Job Search Results")
+    if st.session_state.job_results:
+        filtered_jobs = apply_filters(st.session_state.job_results)
+        if filtered_jobs:
+            st.success(f"Found {len(filtered_jobs)} jobs matching your criteria")
+            if st.session_state.resume_parsed:
+                tech_skills = set(st.session_state.parsed_data.get("technical_skills", []))
+                general_skills = set(st.session_state.parsed_data.get("skills", []))
+                soft_skills = set(st.session_state.parsed_data.get("soft_skills", []))
+                all_skills = tech_skills.union(general_skills).union(soft_skills)
+                for job in filtered_jobs:
+                    if job.get('job_description'):
+                        desc = job.get('job_description', '').lower()
+                        matched_skills = [skill for skill in all_skills if skill.lower() in desc]
+                        match_percentage = int((len(matched_skills) / max(1, len(all_skills))) * 100)
+                        job['match_percentage'] = match_percentage
+                        job['matched_skills'] = matched_skills
+                    else:
+                        job['match_percentage'] = 0
+                        job['matched_skills'] = []
+                sort_by_match = st.checkbox("Sort jobs by skill match percentage", value=True)
+                if sort_by_match:
+                    filtered_jobs = sorted(filtered_jobs, key=lambda x: x.get('match_percentage', 0), reverse=True)
+            for job_idx, job in enumerate(filtered_jobs):
+                if st.session_state.resume_parsed and 'match_percentage' in job:
+                    job_title = f"{job_idx+1}. {job.get('job_title', 'Job Title Not Available')} - {job.get('employer_name', 'Company Not Available')} [Match: {job.get('match_percentage')}%]"
+                else:
+                    job_title = f"{job_idx+1}. {job.get('job_title', 'Job Title Not Available')} - {job.get('employer_name', 'Company Not Available')}"
+                with st.expander(job_title):
+                    cols = st.columns([2, 1])
+                    with cols[0]:
+                        st.write(f"**Company:** {job.get('employer_name', 'Not Available')}")
+                        st.write(f"**Location:** {job.get('job_city', 'Not Available')}, {job.get('job_country', 'Not Available')}")
+                        st.write(f"**Employment Type:** {job.get('job_employment_type', 'Not Available')}")
+                        st.write(f"**Remote:** {'Yes' if job.get('job_is_remote') else 'No'}")
+                        if job.get('job_posted_at_datetime_utc'):
+                            st.write(f"**Posted:** {job.get('job_posted_at_datetime_utc', 'Not Available')}")
+                        if job.get('job_min_salary') and job.get('job_max_salary'):
+                            st.write(f"**Salary Range:** ${job.get('job_min_salary', 'Not Available')} - ${job.get('job_max_salary', 'Not Available')} {job.get('job_salary_currency', 'USD')}")
+                    with cols[1]:
+                        if st.session_state.resume_parsed:
+                            match_percentage = job.get('match_percentage', 0)
+                            matched_skills = job.get('matched_skills', [])
+                            st.markdown("### Skills Match")
+                            bar_color = "green" if match_percentage > 70 else "orange" if match_percentage > 40 else "red"
+                            st.progress(match_percentage / 100)
+                            st.markdown(f"<h4 style='color:{bar_color};margin-top:0'>{match_percentage}% Match</h4>", unsafe_allow_html=True)
+                            if matched_skills:
+                                st.markdown("**Matching Skills:**")
+                                skill_cols = st.columns(2)
+                                for skill_idx, skill in enumerate(matched_skills[:10]):
+                                    col_idx = skill_idx % 2
+                                    with skill_cols[col_idx]:
+                                        st.markdown(f"✅ {skill}")
+                                if len(matched_skills) > 10:
+                                    st.markdown(f"*...and {len(matched_skills)-10} more*")
+                            else:
+                                st.write("⚠️ No direct skill matches found")
+                    st.markdown("**Job Description:**")
+                    full_desc = job.get('job_description', 'No description available')
+                    if len(full_desc) > 1000:
+                        st.markdown(full_desc[:1000] + "...")
+                        if st.button(f"Show Full Description for Job {job_idx+1}", key=f"show_desc_{job_idx}"):
+                            st.markdown(full_desc)
+                    else:
+                        st.markdown(full_desc)
+                    st.markdown("**Apply Links:**")
+                    apply_options = job.get('apply_options', [])
+                    if apply_options:
+                        for option in apply_options:
+                            st.markdown(f"[Apply on {option.get('publisher', 'Job Board')}]({option.get('apply_link')})")
+                    elif job.get('job_apply_link'):
+                        st.markdown(f"[Apply for this job]({job.get('job_apply_link')})")
+        else:
+            st.info("No jobs match your filters. Try adjusting your filter criteria.")
+    else:
+        st.info("No jobs found matching your search criteria. Try adjusting your search terms or location.")
+
+st.markdown("---")
+st.markdown("### How to use this app")
+st.markdown("""
+1. Upload your resume in PDF format to extract your skills and experience  
+2. Enter your job search query and preferred location  
+3. Review job listings and apply directly to positions you're interested in  
+""")
+
+# Display app statistics
+st.sidebar.markdown("### App Statistics")
+if st.session_state.resume_parsed:
+    st.sidebar.success("✅ Resume Parsed")
+    skill_count = len(st.session_state.parsed_data.get("skills", [])) + len(st.session_state.parsed_data.get("technical_skills", []))
+    st.sidebar.metric("Skills Detected", skill_count)
+else:
+    st.sidebar.warning("❌ No Resume Uploaded")
+if st.session_state.search_completed:
+    st.sidebar.success("✅ Job Search Completed")
+    st.sidebar.metric("Jobs Found", len(st.session_state.job_results))
+else:
+    st.sidebar.warning("❌ No Search Performed")
+
+# ----------------------------
+# Step 3: RAG Chatbot Interface
+# ----------------------------
+st.markdown("---")
+st.subheader("Chat with Job Data (RAG Chatbot)")
+
+# Initialize RAG session state variables
+if 'rag_system' not in st.session_state:
+    API_KEY = 'AIzaSyAOK9vRTSRQzd22B2gmbiuIePbZTDyaGYs'
+    st.session_state.rag_system = SimpleRAG(api_key=API_KEY)
+if 'rag_initialized' not in st.session_state:
+    st.session_state.rag_initialized = False
+if 'rag_chat_history' not in st.session_state:
+    st.session_state.rag_chat_history = []
+
+# Button to load job search data into the RAG system
+if st.button("Load Job Data into Chatbot"):
+    if st.session_state.job_results:
+        with st.spinner("Processing job data for chatbot..."):
+            result = st.session_state.rag_system.process_search_data(st.session_state.job_results)
+            if result['status'] == 'success':
+                st.success(result['message'])
+                st.session_state.rag_initialized = True
+            else:
+                st.error(result['message'])
+    else:
+        st.warning("No job data available. Please perform a job search first.")
+
+# Chat input form
+with st.form("rag_chat_form", clear_on_submit=True):
+    user_question = st.text_input("Ask a question about the job data")
+    submit_chat = st.form_submit_button("Send")
+
+if submit_chat and user_question:
+    if st.session_state.rag_initialized:
+        st.session_state.rag_chat_history.append({"user": user_question})
+        with st.spinner("Querying chatbot..."):
+            result = st.session_state.rag_system.query(user_question)
+        if result["status"] == "success":
+            bot_answer = result["answer"]
+            st.session_state.rag_chat_history.append({"bot": bot_answer})
+        else:
+            st.session_state.rag_chat_history.append({"bot": "Error: " + result.get("message", "Unknown error")})
+    else:
+        st.error("RAG system not initialized. Please load job data into the chatbot first.")
+
+# Display chat history
+st.markdown('<div class="chat-box">', unsafe_allow_html=True)
+for msg in st.session_state.rag_chat_history:
+    if "user" in msg:
+        st.markdown(f"<p class='user-message'>User: {msg['user']}</p>", unsafe_allow_html=True)
+    elif "bot" in msg:
+        st.markdown(f"<p class='bot-message'>Bot: {msg['bot']}</p>", unsafe_allow_html=True)
+st.markdown('</div>', unsafe_allow_html=True)

main.py

@@ -0,0 +1,720 @@
+import streamlit as st
+import http.client
+import json
+import os
+import PyPDF2
+import io
+from google import genai
+import requests
+from dotenv import load_dotenv
+import time
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Configure page
+st.set_page_config(page_title="AI Job Finder", page_icon="💼", layout="wide")
+
+# Styling
+st.markdown("""
+<style>
+    /* Global typography */
+    body {
+        font-family: 'Inter', sans-serif;
+    }
+    
+    /* Headers */
+    .main-header {
+        font-size: 2.8rem;
+        font-weight: 700;
+        color: #1E3A8A;
+        margin-bottom: 0.2rem;
+        background: linear-gradient(90deg, #1E3A8A, #3B82F6);
+        -webkit-background-clip: text;
+        -webkit-text-fill-color: transparent;
+    }
+    
+    .sub-header {
+        font-size: 1.2rem;
+        color: #64748B;
+        margin-bottom: 2rem;
+        font-weight: 400;
+    }
+    
+    /* Section headers */
+    .section-header {
+        font-size: 1.5rem;
+        font-weight: 600;
+        color: #1E3A8A;
+        margin-top: 1.5rem;
+        margin-bottom: 1rem;
+        padding-bottom: 0.5rem;
+        border-bottom: 2px solid #E2E8F0;
+    }
+    
+    /* Cards and containers */
+    .card {
+        background-color: #FFFFFF;
+        border-radius: 8px;
+        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.05);
+        padding: 1.5rem;
+        margin-bottom: 1.5rem;
+        border: 1px solid #E2E8F0;
+    }
+    
+    .info-box {
+        background-color: #EFF6FF;
+        border-left: 6px solid #3B82F6;
+        padding: 1rem;
+        border-radius: 4px;
+        margin-bottom: 1.5rem;
+    }
+    
+    /* Messages */
+    .success-message {
+        background-color: #ECFDF5;
+        color: #065F46;
+        padding: 1rem;
+        border-radius: 4px;
+        margin-bottom: 1.5rem;
+        display: flex;
+        align-items: center;
+    }
+    
+    .success-message:before {
+        content: "✅";
+        margin-right: 0.75rem;
+        font-size: 1.2rem;
+    }
+    
+    .warning-message {
+        background-color: #FFFBEB;
+        color: #92400E;
+        padding: 1rem;
+        border-radius: 4px;
+        margin-bottom: 1.5rem;
+        display: flex;
+        align-items: center;
+    }
+    
+    .warning-message:before {
+        content: "⚠️";
+        margin-right: 0.75rem;
+        font-size: 1.2rem;
+    }
+    
+    /* Buttons */
+    .stButton>button {
+        background-color: #3B82F6;
+        color: white;
+        font-weight: 500;
+        border-radius: 6px;
+        padding: 0.5rem 1.5rem;
+        transition: all 0.2s ease;
+    }
+    
+    .stButton>button:hover {
+        background-color: #1D4ED8;
+        box-shadow: 0 4px 6px rgba(29, 78, 216, 0.15);
+    }
+    
+    /* Job listings */
+    .job-card {
+        border-left: 5px solid #3B82F6;
+        background-color: #F8FAFC;
+        padding: 1rem;
+        margin-bottom: 1rem;
+        border-radius: 0 4px 4px 0;
+    }
+    
+    .job-title {
+        font-size: 1.2rem;
+        font-weight: 600;
+        color: #1E3A8A;
+        margin-bottom: 0.5rem;
+    }
+    
+    .job-company {
+        font-size: 1rem;
+        color: #64748B;
+        margin-bottom: 0.5rem;
+    }
+    
+    .job-detail {
+        display: flex;
+        align-items: center;
+        margin-bottom: 0.3rem;
+        font-size: 0.9rem;
+    }
+    
+    .job-detail:before {
+        content: "•";
+        color: #3B82F6;
+        margin-right: 0.5rem;
+    }
+    
+    /* Match indicators */
+    .match-indicator {
+        background-color: #F0F9FF;
+        border-radius: 6px;
+        padding: 1rem;
+    }
+    
+    .match-high {
+        color: #047857;
+        font-weight: 600;
+    }
+    
+    .match-medium {
+        color: #B45309;
+        font-weight: 600;
+    }
+    
+    .match-low {
+        color: #DC2626;
+        font-weight: 600;
+    }
+    
+    .skill-tag {
+        display: inline-block;
+        background-color: #E0F2FE;
+        color: #0369A1;
+        font-size: 0.8rem;
+        padding: 0.25rem 0.75rem;
+        border-radius: 9999px;
+        margin-right: 0.5rem;
+        margin-bottom: 0.5rem;
+    }
+    
+    /* Form elements */
+    .stTextInput>div>div>input {
+        border-radius: 6px;
+        border: 1px solid #CBD5E1;
+    }
+    
+    .stFileUploader>div>button {
+        background-color: #F1F5F9;
+        color: #475569;
+        border-radius: 6px;
+    }
+    
+    /* Sidebar */
+    .sidebar .sidebar-content {
+        background-color: #F8FAFC;
+    }
+    
+    /* Expander */
+    .streamlit-expanderHeader {
+        font-weight: 600;
+        color: #1E3A8A;
+        background-color: #F1F5F9;
+        border-radius: 4px;
+    }
+    
+    /* Dividers */
+    hr {
+        margin: 2rem 0;
+        border: 0;
+        height: 1px;
+        background: #E2E8F0;
+    }
+    
+    /* Progress bar */
+    .stProgress > div > div > div > div {
+        background-color: #3B82F6;
+    }
+    
+    /* Metrics */
+    .metric-card {
+        background-color: #F1F5F9;
+        border-radius: 8px;
+        padding: 1rem;
+        text-align: center;
+        box-shadow: 0 1px 3px rgba(0, 0, 0, 0.05);
+    }
+    
+    .metric-value {
+        font-size: 2rem;
+        font-weight: 700;
+        color: #1E3A8A;
+    }
+    
+    .metric-label {
+        font-size: 0.9rem;
+        color: #64748B;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# Header
+st.markdown('<p class="main-header">AI-Powered Job Finder</p>', unsafe_allow_html=True)
+st.markdown('<p class="sub-header">Upload your resume and find relevant jobs tailored to your skills and experience</p>', unsafe_allow_html=True)
+
+# Initialize session state variables
+if 'resume_text' not in st.session_state:
+    st.session_state.resume_text = ""
+if 'resume_parsed' not in st.session_state:
+    st.session_state.resume_parsed = False
+if 'parsed_data' not in st.session_state:
+    st.session_state.parsed_data = {}
+if 'job_results' not in st.session_state:
+    st.session_state.job_results = []
+if 'search_completed' not in st.session_state:
+    st.session_state.search_completed = False
+
+# Define the JSON schema for resume parsing
+RESUME_SCHEMA = {
+    "schema": {
+        "basic_info": {
+            "name": "string",
+            "email": "string",
+            "phone": "string",
+            "location": "string"
+        },
+        "professional_summary": "string",
+        "skills": ["string"],
+        "technical_skills": ["string"],
+        "soft_skills": ["string"],
+        "experience": [{
+            "job_title": "string",
+            "company": "string",
+            "duration": "string",
+            "description": "string"
+        }],
+        "education": [{
+            "degree": "string",
+            "institution": "string",
+            "year": "string"
+        }],
+        "certifications": ["string"],
+        "years_of_experience": "number"
+    }
+}
+
+# Function to extract text from PDF
+def extract_text_from_pdf(pdf_file):
+    pdf_reader = PyPDF2.PdfReader(pdf_file)
+    text = ""
+    for page_num in range(len(pdf_reader.pages)):
+        text += pdf_reader.pages[page_num].extract_text()
+    return text
+
+# Function to parse resume with Gemini
+def parse_resume_with_gemini(resume_text):
+    try:
+        # Configure the Gemini API
+        client = genai.Client(api_key=os.getenv('GEMINI_API_KEY'))
+        
+        # Construct the prompt with schema
+        prompt = f"""
+        Parse the following resume text and extract information according to this exact JSON schema:
+        
+        {json.dumps(RESUME_SCHEMA, indent=2)}
+        
+        Resume text:
+        {resume_text}
+        
+        Make sure to follow the schema exactly. If any information is not available, use empty strings or empty arrays as appropriate.
+        Return ONLY the JSON object with no additional text.
+        """
+        
+        # Generate the response
+        response = client.models.generate_content(model="gemini-2.0-flash", contents=prompt)
+        
+        # Parse the response to get JSON
+        try:
+            parsed_data = json.loads(response.text)
+            return parsed_data
+        except json.JSONDecodeError:
+            # Try to extract JSON from the text if not directly parseable
+            import re
+            json_match = re.search(r'```json\n(.*?)\n```', response.text, re.DOTALL)
+            if json_match:
+                return json.loads(json_match.group(1))
+            else:
+                st.error("Could not parse the response as JSON")
+                return RESUME_SCHEMA["schema"]
+    except Exception as e:
+        st.error(f"Error parsing resume: {str(e)}")
+        return RESUME_SCHEMA["schema"]
+
+# Function to search for jobs
+def search_jobs(query, location="", page=1):
+    try:
+        conn = http.client.HTTPSConnection("jsearch.p.rapidapi.com")
+        
+        # Format the query string
+        search_query = query.replace(" ", "%20")
+        if location:
+            search_query += f"%20in%20{location.replace(' ', '%20')}"
+        
+        headers = {
+            'X-RapidAPI-Key': os.getenv('RAPIDAPI_KEY'),
+            'X-RapidAPI-Host': "jsearch.p.rapidapi.com"
+        }
+        
+        conn.request("GET", f"/search?query={search_query}&page={page}&num_pages=1", headers=headers)
+        
+        res = conn.getresponse()
+        data = res.read()
+        
+        return json.loads(data.decode("utf-8"))
+    except Exception as e:
+        st.error(f"Error searching for jobs: {str(e)}")
+        return {"data": []}
+
+if 'filter_remote_only' not in st.session_state:
+    st.session_state.filter_remote_only = False
+if 'filter_employment_types' not in st.session_state:
+    st.session_state.filter_employment_types = []
+if 'filter_date_posted' not in st.session_state:
+    st.session_state.filter_date_posted = 0
+if 'min_salary' not in st.session_state:
+    st.session_state.min_salary = 0
+if 'max_salary' not in st.session_state:
+    st.session_state.max_salary = 1000000
+if 'filter_company_types' not in st.session_state:
+    st.session_state.filter_company_types = []
+    
+# Function to apply filters to job results
+def apply_filters(jobs):
+    filtered_jobs = []
+    
+    for job in jobs:
+        # Check remote filter
+        if st.session_state.filter_remote_only and not job.get('job_is_remote', False):
+            continue
+            
+        # Check employment type filter
+        if st.session_state.filter_employment_types and job.get('job_employment_type') not in st.session_state.filter_employment_types:
+            continue
+            
+        # Check date posted filter (in days)
+        if st.session_state.filter_date_posted > 0:
+            current_time = int(time.time())
+            posted_time = job.get('job_posted_at_timestamp', 0)
+            days_ago = (current_time - posted_time) / (60 * 60 * 24)
+            if days_ago > st.session_state.filter_date_posted:
+                continue
+                
+        # Check salary filter
+        if job.get('job_min_salary') is not None and job.get('job_min_salary') < st.session_state.min_salary:
+            continue
+            
+        if job.get('job_max_salary') is not None and job.get('job_max_salary') > st.session_state.max_salary:
+            continue
+            
+        # Check company type filter
+        if st.session_state.filter_company_types and job.get('employer_company_type') not in st.session_state.filter_company_types:
+            continue
+            
+        # All filters passed, add job to filtered results
+        filtered_jobs.append(job)
+        
+    return filtered_jobs
+
+# Main layout
+col1, col2 = st.columns([3, 1])
+
+with col1:
+    # Resume Upload Section
+    st.markdown('<p class="section-header">📄 Upload Your Resume</p>', unsafe_allow_html=True)
+    
+    st.markdown('<div class="info-box">Upload your resume to enable AI-powered job matching based on your skills and experience</div>', unsafe_allow_html=True)
+    
+    uploaded_file = st.file_uploader("Upload your resume (PDF format)", type=['pdf'])
+
+    if uploaded_file is not None:
+        with st.spinner('Processing your resume...'):
+            # Extract text from the PDF
+            resume_text = extract_text_from_pdf(uploaded_file)
+            st.session_state.resume_text = resume_text
+            
+            # Parse the resume
+            parsed_data = parse_resume_with_gemini(resume_text)
+            st.session_state.parsed_data = parsed_data
+            st.session_state.resume_parsed = True
+            
+            # Display success message
+            st.markdown('<div class="success-message">Resume successfully parsed!</div>', unsafe_allow_html=True)
+            
+            # Display the parsed information
+            with st.expander("View Parsed Resume Information", expanded=True):
+                tab1, tab2, tab3 = st.tabs(["Basic Info", "Experience", "Skills & Education"])
+                
+                with tab1:
+                    # Basic information card
+                    basic_info = parsed_data.get("basic_info", {})
+                    st.markdown('<div class="card">', unsafe_allow_html=True)
+                    
+                    col1, col2 = st.columns(2)
+                    with col1:
+                        st.markdown(f"**Name:** {basic_info.get('name', 'Not found')}")
+                        st.markdown(f"**Email:** {basic_info.get('email', 'Not found')}")
+                    
+                    with col2:
+                        st.markdown(f"**Phone:** {basic_info.get('phone', 'Not found')}")
+                        st.markdown(f"**Location:** {basic_info.get('location', 'Not found')}")
+                    
+                    if parsed_data.get("professional_summary"):
+                        st.markdown("<hr style='margin: 1rem 0'>", unsafe_allow_html=True)
+                        st.markdown("**Professional Summary:**")
+                        st.markdown(parsed_data.get("professional_summary", ""))
+                    
+                    st.markdown('</div>', unsafe_allow_html=True)
+                
+                with tab2:
+                    if parsed_data.get("experience"):
+                        for exp in parsed_data.get("experience", []):
+                            st.markdown('<div class="card">', unsafe_allow_html=True)
+                            st.markdown(f"<div style='color: #1E3A8A; font-weight: 600; font-size: 1.1rem;'>{exp.get('job_title', 'Role')}</div>", unsafe_allow_html=True)
+                            st.markdown(f"<div style='color: #64748B; font-weight: 500;'>{exp.get('company', 'Company')}</div>", unsafe_allow_html=True)
+                            st.markdown(f"<div style='color: #94A3B8; font-size: 0.9rem; margin-bottom: 0.75rem;'>{exp.get('duration', 'Duration not specified')}</div>", unsafe_allow_html=True)
+                            st.markdown(exp.get('description', 'No description available'))
+                            st.markdown('</div>', unsafe_allow_html=True)
+                    else:
+                        st.info("No experience information found in your resume")
+                
+                with tab3:
+                    col1, col2 = st.columns(2)
+                    
+                    with col1:
+                        st.markdown('<div class="card">', unsafe_allow_html=True)
+                        st.markdown("<strong>Skills</strong>")
+                        
+                        # Technical skills
+                        st.markdown("**Technical Skills:**")
+                        tech_skills = parsed_data.get("technical_skills", [])
+                        if tech_skills:
+                            for skill in tech_skills:
+                                st.markdown(f"<span class='skill-tag'>{skill}</span>", unsafe_allow_html=True)
+                        else:
+                            st.markdown("No technical skills found")
+                        
+                        # Soft skills
+                        st.markdown("<div style='margin-top: 1rem;'>**Soft Skills:**</div>", unsafe_allow_html=True)
+                        soft_skills = parsed_data.get("soft_skills", [])
+                        if soft_skills:
+                            for skill in soft_skills:
+                                st.markdown(f"<span class='skill-tag'>{skill}</span>", unsafe_allow_html=True)
+                        else:
+                            st.markdown("No soft skills found")
+                        
+                        # General skills
+                        st.markdown("<div style='margin-top: 1rem;'>**General Skills:**</div>", unsafe_allow_html=True)
+                        skills = parsed_data.get("skills", [])
+                        if skills:
+                            for skill in skills:
+                                st.markdown(f"<span class='skill-tag'>{skill}</span>", unsafe_allow_html=True)
+                        else:
+                            st.markdown("No general skills found")
+                            
+                        st.markdown(f"<div style='margin-top: 1rem;'>**Years of Experience:** {parsed_data.get('years_of_experience', 'Not specified')}</div>", unsafe_allow_html=True)
+                        st.markdown('</div>', unsafe_allow_html=True)
+                    
+                    with col2:
+                        st.markdown('<div class="card">', unsafe_allow_html=True)
+                        st.markdown("<strong>Education</strong>")
+                        
+                        for edu in parsed_data.get("education", []):
+                            st.markdown(f"<div style='margin-bottom: 1rem;'>", unsafe_allow_html=True)
+                            st.markdown(f"<div style='font-weight: 600;'>{edu.get('degree', 'Degree')}</div>", unsafe_allow_html=True)
+                            st.markdown(f"<div>{edu.get('institution', 'Institution')}</div>", unsafe_allow_html=True)
+                            st.markdown(f"<div style='color: #94A3B8; font-size: 0.9rem;'>{edu.get('year', 'Year not specified')}</div>", unsafe_allow_html=True)
+                            st.markdown("</div>", unsafe_allow_html=True)
+                            
+                        # Certifications if available
+                        if parsed_data.get("certifications"):
+                            st.markdown("<hr style='margin: 1rem 0'>", unsafe_allow_html=True)
+                            st.markdown("<strong>Certifications</strong>")
+                            for cert in parsed_data.get("certifications", []):
+                                st.markdown(f"• {cert}")
+                        
+                        st.markdown('</div>', unsafe_allow_html=True)
+
+    # Job Search Section
+    st.markdown('<p class="section-header">🔍 Search for Jobs</p>', unsafe_allow_html=True)
+    
+    # Search form with improved styling
+    st.markdown('<div class="card">', unsafe_allow_html=True)
+    
+    search_query = st.text_input("Job Title", placeholder="e.g., Python Developer, Product Manager")
+    
+    col1, col2 = st.columns(2)
+    with col1:
+        location = st.text_input("Location", placeholder="e.g., New York, Remote")
+    with col2:
+        search_button = st.button("Search Jobs", use_container_width=True)
+    
+    if st.session_state.resume_parsed:
+        st.markdown('<div class="success-message">Resume skills will be used for job matching</div>', unsafe_allow_html=True)
+    
+    st.markdown('</div>', unsafe_allow_html=True)
+    
+    if search_button:
+        if search_query:
+            with st.spinner('Searching for relevant jobs...'):
+                # Search for jobs
+                job_results = search_jobs(search_query, location)
+                
+                # Store the results in session state
+                st.session_state.job_results = job_results.get('data', [])
+                st.session_state.search_completed = True
+        else:
+            st.markdown('<div class="warning-message">Please enter a job title to search</div>', unsafe_allow_html=True)
+
+with col2:
+    # Filter sidebar
+    st.markdown('<p class="section-header">⚙️ Filters</p>', unsafe_allow_html=True)
+    
+    st.markdown('<div class="card">', unsafe_allow_html=True)
+    
+    # Remote work filter
+    st.checkbox("Remote Only", key="filter_remote_only")
+    
+    # Employment type filter
+    st.markdown("<div style='margin-top: 1rem;'><strong>Employment Type</strong></div>", unsafe_allow_html=True)
+    employment_types = ["FULLTIME", "PARTTIME", "CONTRACTOR", "INTERN"]
+    st.multiselect(
+        "Select types", 
+        employment_types,
+        default=None,
+        key="filter_employment_types",
+        label_visibility="collapsed"
+    )
+    
+    # Date posted filter
+    st.markdown("<div style='margin-top: 1rem;'><strong>Date Posted</strong></div>", unsafe_allow_html=True)
+    date_options = {
+        "Any time": 0,
+        "Past 24 hours": 1,
+        "Past week": 7,
+        "Past month": 30
+    }
+    selected_date = st.selectbox(
+        "Select timeframe",
+        options=list(date_options.keys()),
+        index=0,
+        label_visibility="collapsed"
+    )
+    st.session_state.filter_date_posted = date_options[selected_date]
+    
+    # Salary range filter
+    st.markdown("<div style='margin-top: 1rem;'><strong>Salary Range</strong></div>", unsafe_allow_html=True)
+    col1, col2 = st.columns(2)
+    with col1:
+        st.number_input("Min (₹)", value=0, step=10000, key="min_salary")
+    with col2:
+        st.number_input("Max (₹)", value=1000000, step=10000, key="max_salary")
+    
+    # Company type filter
+    st.markdown("<div style='margin-top: 1rem;'><strong>Company Type</strong></div>", unsafe_allow_html=True)
+    company_types = ["Public", "Private", "Nonprofit", "Government", "Startup", "Other"]
+    st.multiselect(
+        "Select types",
+        company_types,
+        default=None,
+        key="filter_company_types",
+        label_visibility="collapsed"
+    )
+    
+    st.markdown('</div>', unsafe_allow_html=True)
+    
+    # App metrics
+    st.markdown('<p class="section-header">📊 Stats</p>', unsafe_allow_html=True)
+    
+    st.markdown('<div class="card">', unsafe_allow_html=True)
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        if st.session_state.resume_parsed:
+            skill_count = len(st.session_state.parsed_data.get("skills", [])) + len(st.session_state.parsed_data.get("technical_skills", []))
+            st.markdown('<div class="metric-card">', unsafe_allow_html=True)
+            st.markdown(f'<div class="metric-value">{skill_count}</div>', unsafe_allow_html=True)
+            st.markdown('<div class="metric-label">Skills</div>', unsafe_allow_html=True)
+            st.markdown('</div>', unsafe_allow_html=True)
+        else:
+            st.markdown('<div class="metric-card" style="opacity: 0.5">', unsafe_allow_html=True)
+            st.markdown('<div class="metric-value">-</div>', unsafe_allow_html=True)
+            st.markdown('<div class="metric-label">Skills</div>', unsafe_allow_html=True)
+            st.markdown('</div>', unsafe_allow_html=True)
+    
+    with col2:
+        if st.session_state.search_completed:
+            job_count = len(st.session_state.job_results)
+            st.markdown('<div class="metric-card">', unsafe_allow_html=True)
+            st.markdown(f'<div class="metric-value">{job_count}</div>', unsafe_allow_html=True)
+            st.markdown('<div class="metric-label">Jobs</div>', unsafe_allow_html=True)
+            st.markdown('</div>', unsafe_allow_html=True)
+        else:
+            st.markdown('<div class="metric-card" style="opacity: 0.5">', unsafe_allow_html=True)
+            st.markdown('<div class="metric-value">-</div>', unsafe_allow_html=True)
+            st.markdown('<div class="metric-label">Jobs</div>', unsafe_allow_html=True)
+            st.markdown('</div>', unsafe_allow_html=True)
+    
+    st.markdown('</div>', unsafe_allow_html=True)
+
+# Display Results
+if st.session_state.search_completed:
+    st.markdown('<p class="section-header">🎯 Job Matches</p>', unsafe_allow_html=True)
+    
+    if st.session_state.job_results:
+        # Apply filters
+        filtered_jobs = apply_filters(st.session_state.job_results)
+        
+        if filtered_jobs:
+            st.markdown(f'<div class="success-message">Found {len(filtered_jobs)} jobs matching your criteria</div>', unsafe_allow_html=True)
+            
+            # Calculate skill match percentages if resume is uploaded
+            if st.session_state.resume_parsed:
+                # Extract all skills from resume
+                tech_skills = set(st.session_state.parsed_data.get("technical_skills", []))
+                general_skills = set(st.session_state.parsed_data.get("skills", []))
+                soft_skills = set(st.session_state.parsed_data.get("soft_skills", []))
+                all_skills = tech_skills.union(general_skills).union(soft_skills)
+                
+                # Add match score to each job
+                for job in filtered_jobs:
+                    if job.get('job_description'):
+                        desc = job.get('job_description', '').lower()
+                        matched_skills = [skill for skill in all_skills if skill.lower() in desc]
+                        match_percentage = int((len(matched_skills) / max(1, len(all_skills))) * 100)
+                        job['match_percentage'] = match_percentage
+                        job['matched_skills'] = matched_skills
+                    else:
+                        job['match_percentage'] = 0
+                        job['matched_skills'] = []
+                
+                # Option to sort by match percentage
+                col1, col2 = st.columns([1, 2])
+                with col1:
+                    sort_by_match = st.checkbox("Sort by match percentage", value=True)
+                
+                if sort_by_match:
+                    filtered_jobs = sorted(filtered_jobs, key=lambda x: x.get('match_percentage', 0), reverse=True)
+            
+            for job_idx, job in enumerate(filtered_jobs):
+                # Create a job card
+                st.markdown('<div class="card job-card">', unsafe_allow_html=True)
+                
+                # Job header
+                cols = st.columns([3, 1])
+                
+                with cols[0]:
+                    if st.session_state.resume_parsed and 'match_percentage' in job:
+                        match_percentage = job.get('match_percentage', 0)
+                        if match_percentage > 70:
+                            match_class = "match-high"
+                        elif match_percentage > 40:
+                            match_class = "match-medium"
+                        else:
+                            match_class = "match-low"
+                            
+                        st.markdown(f"<div style='display: flex; align-items: center;'>", unsafe_allow_html=True)
+                        st.markdown(f"<div class='job-title'>{job.get('job_title', 'Job Title Not Available')}</div>", unsafe_allow_html=True)
+                        st.markdown(f"<div style='margin-left: 0.75rem; padding: 0.2rem 0.5rem; border-radius: 4px; font-size: 0.8rem;' class='{match_class}'>{match_percentage}% Match</div>", unsafe_allow_html=True)
+                        st.markdown("</div>", unsafe_allow_html=True)
+                    else:
+                        st.markdown(f"<div class='job-title'>{job.get('job_title', 'Job Title Not Available')}</div>", unsafe_allow_html=True)
+                    
+                    st.markdown(f"<div class='job-company'>{job.get('employer_name', 'Company Not Available')}</div>", unsafe_allow_html=True)

main2.py

@@ -0,0 +1,499 @@
+import streamlit as st
+import http.client
+import json
+import os
+import PyPDF2
+import io
+from google import genai
+import requests
+from dotenv import load_dotenv
+import time
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Configure page
+st.set_page_config(page_title="AI Job Finder", page_icon="💼", layout="wide")
+
+# Styling
+st.markdown("""
+<style>
+    .main-header {
+        font-size: 2.5rem;
+        color: #4169E1;
+    }
+    .sub-header {
+        font-size: 1.5rem;
+        color: #6C757D;
+    }
+    .success-message {
+        background-color: #D4EDDA;
+        color: #155724;
+        padding: 10px;
+        border-radius: 5px;
+        margin-bottom: 20px;
+    }
+    .info-box {
+        background-color: #E7F3FE;
+        border-left: 6px solid #2196F3;
+        padding: 10px;
+        margin-bottom: 15px;
+    }
+    .search-options {
+        margin-top: 20px;
+        margin-bottom: 20px;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# Header
+st.markdown('<p class="main-header">AI-Powered Job Finder</p>', unsafe_allow_html=True)
+st.markdown('<p class="sub-header">Upload your resume and find relevant jobs</p>', unsafe_allow_html=True)
+
+# Initialize session state variables
+if 'resume_text' not in st.session_state:
+    st.session_state.resume_text = ""
+if 'resume_parsed' not in st.session_state:
+    st.session_state.resume_parsed = False
+if 'parsed_data' not in st.session_state:
+    st.session_state.parsed_data = {}
+if 'job_results' not in st.session_state:
+    st.session_state.job_results = []
+if 'search_completed' not in st.session_state:
+    st.session_state.search_completed = False
+
+# Define the JSON schema for resume parsing
+RESUME_SCHEMA = {
+    "schema": {
+        "basic_info": {
+            "name": "string",
+            "email": "string",
+            "phone": "string",
+            "location": "string"
+        },
+        "professional_summary": "string",
+        "skills": ["string"],
+        "technical_skills": ["string"],
+        "soft_skills": ["string"],
+        "experience": [{
+            "job_title": "string",
+            "company": "string",
+            "duration": "string",
+            "description": "string"
+        }],
+        "education": [{
+            "degree": "string",
+            "institution": "string",
+            "year": "string"
+        }],
+        "certifications": ["string"],
+        "years_of_experience": "number"
+    }
+}
+
+# Function to extract text from PDF
+def extract_text_from_pdf(pdf_file):
+    pdf_reader = PyPDF2.PdfReader(pdf_file)
+    text = ""
+    for page_num in range(len(pdf_reader.pages)):
+        text += pdf_reader.pages[page_num].extract_text()
+    return text
+
+# Function to parse resume with Gemini
+def parse_resume_with_gemini(resume_text):
+    try:
+        # Configure the Gemini API
+        client = genai.Client(api_key=os.getenv('GEMINI_API_KEY'))
+        
+        # Construct the prompt with schema
+        prompt = f"""
+        Parse the following resume text and extract information according to this exact JSON schema:
+        
+        {json.dumps(RESUME_SCHEMA, indent=2)}
+        
+        Resume text:
+        {resume_text}
+        
+        Make sure to follow the schema exactly. If any information is not available, use empty strings or empty arrays as appropriate.
+        Return ONLY the JSON object with no additional text.
+        """
+        
+        # Get the model
+        
+        # Generate the response
+        response = client.models.generate_content(model="gemini-2.0-flash", contents=prompt)
+
+        
+        # Parse the response to get JSON
+        try:
+            parsed_data = json.loads(response.text)
+            return parsed_data
+        except json.JSONDecodeError:
+            # Try to extract JSON from the text if not directly parseable
+            import re
+            json_match = re.search(r'```json\n(.*?)\n```', response.text, re.DOTALL)
+            if json_match:
+                return json.loads(json_match.group(1))
+            else:
+                st.error("Could not parse the response as JSON")
+                return RESUME_SCHEMA["schema"]
+    except Exception as e:
+        st.error(f"Error parsing resume: {str(e)}")
+        return RESUME_SCHEMA["schema"]
+
+# Function to search for jobs
+def search_jobs(query, location="", page=1):
+    try:
+        conn = http.client.HTTPSConnection("jsearch.p.rapidapi.com")
+        
+        # Format the query string
+        search_query = query.replace(" ", "%20")
+        if location:
+            search_query += f"%20in%20{location.replace(' ', '%20')}"
+        
+        headers = {
+            'X-RapidAPI-Key': os.getenv('RAPIDAPI_KEY'),
+            'X-RapidAPI-Host': "jsearch.p.rapidapi.com"
+        }
+        
+        conn.request("GET", f"/search?query={search_query}&page={page}&num_pages=1", headers=headers)
+        
+        res = conn.getresponse()
+        data = res.read()
+        
+        return json.loads(data.decode("utf-8"))
+    except Exception as e:
+        st.error(f"Error searching for jobs: {str(e)}")
+        return {"data": []}
+
+if 'filter_remote_only' not in st.session_state:
+    st.session_state.filter_remote_only = False
+if 'filter_employment_types' not in st.session_state:
+    st.session_state.filter_employment_types = []
+if 'filter_date_posted' not in st.session_state:
+    st.session_state.filter_date_posted = 0
+if 'min_salary' not in st.session_state:
+    st.session_state.min_salary = 0
+if 'max_salary' not in st.session_state:
+    st.session_state.max_salary = 1000000
+if 'filter_company_types' not in st.session_state:
+    st.session_state.filter_company_types = []
+    
+# Function to apply filters to job results
+def apply_filters(jobs):
+    filtered_jobs = []
+    
+    for job in jobs:
+        # Check remote filter
+        if st.session_state.filter_remote_only and not job.get('job_is_remote', False):
+            continue
+            
+        # Check employment type filter
+        if st.session_state.filter_employment_types and job.get('job_employment_type') not in st.session_state.filter_employment_types:
+            continue
+            
+        # Check date posted filter (in days)
+        if st.session_state.filter_date_posted > 0:
+            current_time = int(time.time())
+            posted_time = job.get('job_posted_at_timestamp', 0)
+            days_ago = (current_time - posted_time) / (60 * 60 * 24)
+            if days_ago > st.session_state.filter_date_posted:
+                continue
+                
+        # Check salary filter
+        if job.get('job_min_salary') is not None and job.get('job_min_salary') < st.session_state.min_salary:
+            continue
+            
+        if job.get('job_max_salary') is not None and job.get('job_max_salary') > st.session_state.max_salary:
+            continue
+            
+        # Check company type filter
+        if st.session_state.filter_company_types and job.get('employer_company_type') not in st.session_state.filter_company_types:
+            continue
+            
+        # All filters passed, add job to filtered results
+        filtered_jobs.append(job)
+        
+    return filtered_jobs
+
+# Resume Upload Section
+st.subheader("Step 1: Upload Your Resume First")
+uploaded_file = st.file_uploader("Upload your resume (PDF format)", type=['pdf'])
+
+if uploaded_file is not None:
+    with st.spinner('Processing your resume...'):
+        # Extract text from the PDF
+        resume_text = extract_text_from_pdf(uploaded_file)
+        st.session_state.resume_text = resume_text
+        
+        # Parse the resume
+        parsed_data = parse_resume_with_gemini(resume_text)
+        st.session_state.parsed_data = parsed_data
+        st.session_state.resume_parsed = True
+        
+        # Display the parsed information
+        with st.expander("Resume Parsed Information", expanded=True):
+            col1, col2 = st.columns(2)
+            
+            with col1:
+                st.markdown("### Basic Information")
+                basic_info = parsed_data.get("basic_info", {})
+                st.write(f"**Name:** {basic_info.get('name', 'Not found')}")
+                st.write(f"**Email:** {basic_info.get('email', 'Not found')}")
+                st.write(f"**Phone:** {basic_info.get('phone', 'Not found')}")
+                st.write(f"**Location:** {basic_info.get('location', 'Not found')}")
+                
+                st.markdown("### Experience")
+                for exp in parsed_data.get("experience", []):
+                    st.markdown(f"**{exp.get('job_title', 'Role')} at {exp.get('company', 'Company')}**")
+                    st.write(f"*{exp.get('duration', 'Duration not specified')}*")
+                    st.write(exp.get('description', 'No description available'))
+                    st.write("---")
+            
+            with col2:
+                st.markdown("### Skills")
+                
+                # Technical skills
+                st.write("**Technical Skills:**")
+                tech_skills = parsed_data.get("technical_skills", [])
+                if tech_skills:
+                    st.write(", ".join(tech_skills))
+                else:
+                    st.write("No technical skills found")
+                
+                # Soft skills
+                st.write("**Soft Skills:**")
+                soft_skills = parsed_data.get("soft_skills", [])
+                if soft_skills:
+                    st.write(", ".join(soft_skills))
+                else:
+                    st.write("No soft skills found")
+                
+                # General skills
+                st.write("**General Skills:**")
+                skills = parsed_data.get("skills", [])
+                if skills:
+                    st.write(", ".join(skills))
+                else:
+                    st.write("No general skills found")
+                
+                st.markdown("### Education")
+                for edu in parsed_data.get("education", []):
+                    st.write(f"**{edu.get('degree', 'Degree')}** - {edu.get('institution', 'Institution')}")
+                    st.write(f"*{edu.get('year', 'Year not specified')}*")
+                
+                st.write(f"**Years of Experience:** {parsed_data.get('years_of_experience', 'Not specified')}")
+st.markdown("---")
+st.subheader("Step 2: Search for Jobs")
+
+# Query input
+search_query = st.text_input("Enter your job search query (e.g., 'Python Developer')")
+location = st.text_input("Location (e.g., 'New York', 'Remote')")
+
+# Add filter options to sidebar
+st.sidebar.markdown("### Filter Options")
+
+# Remote work filter
+st.sidebar.checkbox("Remote Only", key="filter_remote_only")
+
+# Employment type filter
+employment_types = ["FULLTIME", "PARTTIME", "CONTRACTOR", "INTERN"]
+st.sidebar.multiselect(
+    "Employment Type", 
+    employment_types,
+    default=None,
+    key="filter_employment_types"
+)
+
+# Date posted filter
+date_options = {
+    "Any time": 0,
+    "Past 24 hours": 1,
+    "Past week": 7,
+    "Past month": 30
+}
+selected_date = st.sidebar.selectbox(
+    "Date Posted",
+    options=list(date_options.keys()),
+    index=0
+)
+st.session_state.filter_date_posted = date_options[selected_date]
+
+# Salary range filter (only if salary data is available)
+st.sidebar.markdown("### Salary Range")
+col1, col2 = st.sidebar.columns(2)
+with col1:
+    st.number_input("Min ($)", value=0, step=10000, key="min_salary")
+with col2:
+    st.number_input("Max ($)", value=1000000, step=10000, key="max_salary")
+
+# Company type filter
+company_types = ["Public", "Private", "Nonprofit", "Government", "Startup", "Other"]
+st.sidebar.multiselect(
+    "Company Type", 
+    company_types,
+    default=None,
+    key="filter_company_types"
+)
+
+if st.button("Search Jobs"):
+    if search_query:
+        with st.spinner('Searching for jobs...'):
+            final_query = search_query
+            
+            # Search for jobs
+            job_results = search_jobs(final_query, location)
+            
+            # Store the results in session state
+            st.session_state.job_results = job_results.get('data', [])
+            st.session_state.search_completed = True
+    else:
+        st.warning("Please enter a search query")
+
+# Display Results
+if st.session_state.search_completed:
+    st.markdown("---")
+    st.subheader("Job Search Results")
+    
+    if st.session_state.job_results:
+        # Apply filters
+        filtered_jobs = apply_filters(st.session_state.job_results)
+        
+        if filtered_jobs:
+            st.success(f"Found {len(filtered_jobs)} jobs matching your criteria")
+            
+            # Calculate skill match percentages if resume is uploaded
+            if st.session_state.resume_parsed:
+                # Extract all skills from resume
+                tech_skills = set(st.session_state.parsed_data.get("technical_skills", []))
+                general_skills = set(st.session_state.parsed_data.get("skills", []))
+                soft_skills = set(st.session_state.parsed_data.get("soft_skills", []))
+                all_skills = tech_skills.union(general_skills).union(soft_skills)
+                
+                # Add match score to each job
+                for job in filtered_jobs:
+                    if job.get('job_description'):
+                        desc = job.get('job_description', '').lower()
+                        matched_skills = [skill for skill in all_skills if skill.lower() in desc]
+                        match_percentage = int((len(matched_skills) / max(1, len(all_skills))) * 100)
+                        job['match_percentage'] = match_percentage
+                        job['matched_skills'] = matched_skills
+                    else:
+                        job['match_percentage'] = 0
+                        job['matched_skills'] = []
+                
+                # Option to sort by match percentage
+                sort_by_match = st.checkbox("Sort jobs by skill match percentage", value=True)
+                if sort_by_match:
+                    filtered_jobs = sorted(filtered_jobs, key=lambda x: x.get('match_percentage', 0), reverse=True)
+            
+            for job_idx, job in enumerate(filtered_jobs):
+                # Customize job title based on match percentage if resume uploaded
+                if st.session_state.resume_parsed and 'match_percentage' in job:
+                    job_title = f"{job_idx+1}. {job.get('job_title', 'Job Title Not Available')} - {job.get('employer_name', 'Company Not Available')} "
+                    job_title += f"[Match: {job.get('match_percentage')}%]"
+                else:
+                    job_title = f"{job_idx+1}. {job.get('job_title', 'Job Title Not Available')} - {job.get('employer_name', 'Company Not Available')}"
+                
+                with st.expander(job_title):
+                    cols = st.columns([2, 1])
+                    
+                    with cols[0]:
+                        # Job details
+                        st.write(f"**Company:** {job.get('employer_name', 'Not Available')}")
+                        st.write(f"**Location:** {job.get('job_city', 'Not Available')}, {job.get('job_country', 'Not Available')}")
+                        st.write(f"**Employment Type:** {job.get('job_employment_type', 'Not Available')}")
+                        
+                        # Remote information
+                        st.write(f"**Remote:** {'Yes' if job.get('job_is_remote') else 'No'}")
+                        
+                        # Date posted and expiration
+                        if job.get('job_posted_at_datetime_utc'):
+                            st.write(f"**Posted:** {job.get('job_posted_at_datetime_utc', 'Not Available')}")
+                        
+                        # Salary information
+                        if job.get('job_min_salary') and job.get('job_max_salary'):
+                            st.write(f"**Salary Range:** ${job.get('job_min_salary', 'Not Available')} - ${job.get('job_max_salary', 'Not Available')} {job.get('job_salary_currency', 'USD')}")
+                    
+                    with cols[1]:
+                        # Enhanced skills match section
+                        if st.session_state.resume_parsed:
+                            match_percentage = job.get('match_percentage', 0)
+                            matched_skills = job.get('matched_skills', [])
+                            
+                            # Create a visual progress bar for match percentage
+                            st.markdown("### Skills Match")
+                            
+                            # Color coding based on match percentage
+                            if match_percentage > 70:
+                                bar_color = "green"
+                            elif match_percentage > 40:
+                                bar_color = "orange"
+                            else:
+                                bar_color = "red"
+                                
+                            # Display progress bar
+                            st.progress(match_percentage / 100)
+                            st.markdown(f"<h4 style='color:{bar_color};margin-top:0'>{match_percentage}% Match</h4>", unsafe_allow_html=True)
+                            
+                            if matched_skills:
+                                st.markdown("**Matching Skills:**")
+                                skill_cols = st.columns(2)
+                                for skill_idx, skill in enumerate(matched_skills[:10]):  # Changed variable name from i to skill_idx
+                                    col_idx = skill_idx % 2
+                                    with skill_cols[col_idx]:
+                                        st.markdown(f"✅ {skill}")
+                                        
+                                if len(matched_skills) > 10:
+                                    st.markdown(f"*...and {len(matched_skills)-10} more*")
+                            else:
+                                st.write("⚠️ No direct skill matches found")
+                    
+                    # Description
+                    st.markdown("**Job Description:**")
+                    full_desc = job.get('job_description', 'No description available')
+                    
+                    if len(full_desc) > 1000:
+                        st.markdown(full_desc[:1000] + "...")
+                        if st.button(f"Show Full Description for Job {job_idx+1}", key=f"show_desc_{job_idx}"):
+                            st.markdown(full_desc)
+                    else:
+                        st.markdown(full_desc)
+
+                    # Display ALL application links
+                    st.markdown("**Apply Links:**")
+                    apply_options = job.get('apply_options', [])
+                    if apply_options:
+                        for option in apply_options:
+                            st.markdown(f"[Apply on {option.get('publisher', 'Job Board')}]({option.get('apply_link')})")
+                    elif job.get('job_apply_link'):
+                        st.markdown(f"[Apply for this job]({job.get('job_apply_link')})")
+        else:
+            st.info("No jobs match your filters. Try adjusting your filter criteria.")
+    else:
+        st.info("No jobs found matching your search criteria. Try adjusting your search terms or location.")
+
+
+st.markdown("---")
+st.markdown("### How to use this app")
+st.markdown("""
+1. Upload your resume in PDF format to extract your skills and experience
+2. Enter your job search query and preferred location
+3. Review job listings and apply directly to positions you're interested in
+""")
+
+
+# Display app statistics
+st.sidebar.markdown("### App Statistics")
+if st.session_state.resume_parsed:
+    st.sidebar.success("✅ Resume Parsed")
+    skill_count = len(st.session_state.parsed_data.get("skills", [])) + len(st.session_state.parsed_data.get("technical_skills", []))
+    st.sidebar.metric("Skills Detected", skill_count)
+else:
+    st.sidebar.warning("❌ No Resume Uploaded")
+
+if st.session_state.search_completed:
+    st.sidebar.success("✅ Job Search Completed")
+    st.sidebar.metric("Jobs Found", len(st.session_state.job_results))
+else:
+    st.sidebar.warning("❌ No Search Performed")

rag.py

@@ -0,0 +1,99 @@
+# rag.py
+from sentence_transformers import SentenceTransformer
+import faiss
+import numpy as np
+import google as genai
+import os
+
+class SimpleRAG:
+    def __init__(self, api_key):
+        # Initialize the embedding model and generative AI
+        self.embedder = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+        genai.configure(api_key=api_key)
+        self.model = genai.GenerativeModel("gemini-1.5-flash")
+        self.index = None
+        self.chunks = []
+        self.is_initialized = False
+        self.processing_status = None
+
+    def chunk_text(self, text, chunk_size=700):
+        """Split text into smaller chunks."""
+        words = text.split()
+        return [' '.join(words[i:i + chunk_size]) for i in range(0, len(words), chunk_size)]
+
+    def process_search_data(self, search_data):
+        """
+        Process search result data and index it.
+        'search_data' should be a list of job posting dictionaries.
+        For each job posting, we combine key fields (e.g., job title and description) and then chunk the text.
+        """
+        try:
+            self.processing_status = "Processing search data..."
+            combined_text = ""
+            for job in search_data:
+                # Combine job title and job description (you can add more fields if needed)
+                job_title = job.get('job_title', '')
+                job_description = job.get('job_description', '')
+                combined_text += f"Job Title: {job_title}. Description: {job_description}. "
+
+            if not combined_text.strip():
+                raise Exception("No text found in search results.")
+            
+            # Chunk the combined text
+            self.chunks = self.chunk_text(combined_text)
+            if not self.chunks:
+                raise Exception("No content chunks were generated from search data.")
+            
+            # Generate embeddings and create the FAISS index
+            embeddings = self.embedder.encode(self.chunks)
+            vector_dimension = embeddings.shape[1]
+            self.index = faiss.IndexFlatL2(vector_dimension)
+            self.index.add(np.array(embeddings).astype('float32'))
+            
+            self.is_initialized = True
+            self.processing_status = f"RAG system initialized with {len(self.chunks)} chunks."
+            return {"status": "success", "message": self.processing_status}
+        except Exception as e:
+            self.processing_status = f"Error: {str(e)}"
+            self.is_initialized = False
+            return {"status": "error", "message": str(e)}
+
+    def get_status(self):
+        """Return the current processing status."""
+        return {
+            "is_initialized": self.is_initialized,
+            "status": self.processing_status
+        }
+
+    def get_relevant_chunks(self, query, k=3):
+        """Retrieve the top-k most relevant text chunks for a given query."""
+        query_vector = self.embedder.encode([query])
+        distances, chunk_indices = self.index.search(query_vector.astype('float32'), k)
+        return [self.chunks[i] for i in chunk_indices[0]]
+
+    def query(self, question):
+        """Query the RAG system with a user question."""
+        if not self.is_initialized:
+            raise Exception("RAG system not initialized. Please process search data first.")
+        try:
+            context = self.get_relevant_chunks(question)
+            prompt = f"""
+            Based on the following context, provide a clear and concise answer.
+            If the context doesn't contain enough relevant information, say "I don't have enough information to answer that question."
+
+            Context:
+            {' '.join(context)}
+
+            Question: {question}
+            """
+            response = self.model.generate_content(prompt)
+            return {
+                "status": "success",
+                "answer": response.text.strip(),
+                "context": context
+            }
+        except Exception as e:
+            return {
+                "status": "error",
+                "message": str(e)
+            }

requirements.txt

@@ -0,0 +1,12 @@
+streamlit
+python-dotenv
+PyPDF2
+requests
+sentence-transformers
+faiss-cpu
+numpy
+google-genai
+certifi
+charset-normalizer
+idna
+urllib3