Create new_test.py

new_test.py

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+import PyPDF2  # For PDF text extraction
+import streamlit as st  # For building the Streamlit app
+from docx import Document  # For Word document text extraction
+from langchain.chains import RunnableLambda, RunnablePassthrough
+from langchain.chat_models import ChatGoogleGenerativeAI
+from langchain.memory import ConversationBufferMemory
+from langchain.prompts import (ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder, SystemMessage)
+from langchain.schema import StrOutputParser  # For parsing the output
+
+
+# Function to extract text from PDF
+def extract_text_from_pdf(uploaded_file):
+    text = ""
+    reader = PyPDF2.PdfReader(uploaded_file)
+    for page in reader.pages:
+        text += page.extract_text()
+    return text
+
+# Function to extract text from Word document
+def extract_text_from_word(uploaded_file):
+    text = ""
+    doc = Document(uploaded_file)
+    for paragraph in doc.paragraphs:
+        text += paragraph.text + "\n"
+    return text
+
+
+
+# Initialize Google Generative AI chat model
+def initialize_chat_model():
+    with open("key.txt", "r") as f:
+        GOOGLE_API_KEY = f.read().strip()
+
+    chat_model = ChatGoogleGenerativeAI(
+        google_api_key=GOOGLE_API_KEY,
+        model="gemini-1.5-pro-latest",
+        temperature=0.4,
+        max_tokens=2000,
+        timeout=120,
+        max_retries=5,
+        top_p=0.9,
+        top_k=40,
+        presence_penalty=0.6,
+        frequency_penalty=0.3
+    )
+    return chat_model
+
+chat_model = initialize_chat_model()
+
+# Create Chat Template
+chat_prompt_template = ChatPromptTemplate.from_messages(
+    [
+        SystemMessage(
+            content="""  You are a language model designed to follow user instructions exactly as given.
+            Do not take any actions or provide any information unless specifically directed by the user.
+            Your role is to fulfill the user's requests precisely without deviating from the instructions provided."""
+        ),
+        MessagesPlaceholder(variable_name="chat_history"),
+        HumanMessagePromptTemplate.from_template("{human_input}")
+    ]
+)
+
+# Initialize the Memory
+memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+
+# Create an Output Parser
+output_parser = StrOutputParser()
+
+# Define a chain
+chain = RunnablePassthrough.assign(
+            chat_history=RunnableLambda(lambda human_input: memory.load_memory_variables(human_input)['chat_history'])
+        ) | chat_prompt_template | chat_model | output_parser
+
+# Streamlit App
+st.title("Interview Preparation with AI")
+st.markdown("## Part-1: Upload Files, Summarize, and Extract Keywords")
+
+# File upload section
+file1 = st.file_uploader("Upload your resume (PDF or DOCX):", type=["pdf", "docx"])
+file2 = st.file_uploader("Upload the job description (PDF or DOCX):", type=["pdf", "docx"])
+
+if file1 and file2:
+    try:
+        # Detect file type and extract text for file 1
+        if file1.name.endswith('.pdf'):
+            text1 = extract_text_from_pdf(file1)
+        elif file1.name.endswith('.docx'):
+            text1 = extract_text_from_word(file1)
+        else:
+            st.error("Unsupported file type for file 1")
+
+        # Detect file type and extract text for file 2
+        if file2.name.endswith('.pdf'):
+            text2 = extract_text_from_pdf(file2)
+        elif file2.name.endswith('.docx'):
+            text2 = extract_text_from_word(file2)
+        else:
+            st.error("Unsupported file type for file 2")
+
+
+        # Ensure session state variables are initialized
+
+        if "ats_score_calculated" not in st.session_state:
+            st.session_state.ats_score_calculated = False
+
+
+        # Button to Calculate ATS Score
+        if st.button("ATS Score") or st.session_state.ats_score_calculated:
+            
+            st.session_state.ats_score_calculated = True
+            resume_keywords = set(keywords1)
+            job_description_keywords = set(keywords2)
+            st.markdown("### ATS Score Calculation")
+            query = {"human_input": f"""
+You are an advanced Applicant Tracking System (ATS) designed to evaluate resumes against job descriptions with exceptional accuracy. Analyze the following keywords extracted from a job description and a resume, compare them, and calculate the match percentage.
+Job Description Keywords:
+{list(job_description_keywords)}
+Resume Keywords:
+{list(resume_keywords)}
+Provide the ATS score as a percentage match between the resume and the job description in the following format:
+The ATS Score of your Resume According to the Job Description is \"XX%\".
+"""}
+
+            response = chain.invoke(query)
+            memory.save_context(query, {"output": response})
+
+            st.write(response)
+
+            
+    except Exception as e:
+        st.error(f"An error occurred: {e}")
+
+else:
+    st.info("Please upload both files to proceed.")