Update app.py

app.py

@@ -2,6 +2,7 @@ import streamlit as st
 from docx import Document
 import PyPDF2
 import google.generativeai as genai  # Correct package for Gemini
+import re  # For output validation
 
 # Title of the app
 st.title("JD-Resume Fit Check App")
@@ -20,22 +21,18 @@ with col1:
     st.subheader('Upload your Resume')
     uploaded_file = st.file_uploader('Upload your Resume (PDF or DOCX)', type=['pdf', 'docx'])
     resume_text = ""
-
-    try:
-        if uploaded_file:
-            if uploaded_file.type == 'application/pdf':
-                pdf_reader = PyPDF2.PdfReader(uploaded_file)
-                for page in pdf_reader.pages:
-                    text = page.extract_text()
-                    if text:
-                        resume_text += text
-                st.success("Resume uploaded and processed!")
-            elif uploaded_file.type == 'application/vnd.openxmlformats-officedocument.wordprocessingml.document':
-                doc = Document(uploaded_file)
-                resume_text = '\n'.join([paragraph.text for paragraph in doc.paragraphs if paragraph.text])
-                st.success("Resume uploaded and processed!")
-    except Exception as e:
-        st.error(f"Error processing file: {e}")
+    if uploaded_file is not None:
+        if uploaded_file.type == 'application/pdf':
+            # Extract text from PDF
+            pdf_reader = PyPDF2.PdfReader(uploaded_file)
+            for page in pdf_reader.pages:
+                resume_text += page.extract_text()
+            st.success("Resume uploaded and processed!")
+        elif uploaded_file.type == 'application/vnd.openxmlformats-officedocument.wordprocessingml.document':
+            # Extract text from DOCX
+            doc = Document(uploaded_file)
+            resume_text = '\n'.join([paragraph.text for paragraph in doc.paragraphs])
+            st.success("Resume uploaded and processed!")
 
 # Right column: JD input
 with col2:
@@ -57,9 +54,8 @@ if resume_text and job_description:
     # Truncate input if too large
     max_input_tokens = 4000  # Example limit
     combined_input = f"{resume_text}\n{job_description}"
-    words = combined_input.split()
-    if len(words) > max_input_tokens:
-        combined_input = ' '.join(words[:max_input_tokens])
+    if len(combined_input.split()) > max_input_tokens:
+        combined_input = ' '.join(combined_input.split()[:max_input_tokens])
         st.warning("Input text truncated to fit the model's token limit.")
 
     # Display a "Generate" button
@@ -68,14 +64,13 @@ if resume_text and job_description:
 
         # Construct the prompt for analysis
         prompt = f"""
-        You are an expert recruiter and hiring manager assistant. Analyze the following details and provide a structured response in the specified format:
+        You are an expert recruiter and hiring manager assistant. Analyze the following details and strictly provide the response in the specified format:
 
+        ### Input:
         1. Resume: {resume_text}
-
         2. Job Description: {job_description}
 
         ### Tasks:
-
         1. Identify the key skills, experiences, and qualifications mentioned in the Job Description.
         2. Compare the above with the details provided in the Resume.
         3. Provide a match score (out of 10) based on how well the Resume aligns with the Job Description.
@@ -84,22 +79,36 @@ if resume_text and job_description:
         6. Recommend relevant topics for interview preparation based on the Job Description.
 
         ### Response Format:
-        1. Match Score: [Provide a score out of 10]
-        2. Justification: [Provide a detailed analysis of how well the resume matches the job description]
-        3. Resume Suggestions: [List actionable changes to align the resume with the job description]
-        4. Interview Preparation Topics: [List relevant topics for interview preparation]
+        1. **Match Score:** [Provide a score out of 10]
+        2. **Justification:** [Detailed analysis of how well the resume matches the job description]
+        3. **Resume Suggestions:** [Actionable changes to align the resume with the job description]
+        4. **Interview Preparation Topics:** [Relevant topics for interview preparation]
+
+        Example:
+        1. **Match Score:** 8/10
+        2. **Justification:** The resume covers 80% of the key skills but lacks specific cloud experience.
+        3. **Resume Suggestions:** Add certifications in cloud platforms and mention specific cloud-related projects.
+        4. **Interview Preparation Topics:** Cloud computing, project management, and teamwork.
         """
 
         try:
-            # Initialize the generative model
-            model = genai.GenerativeModel('gemini-pro')
-
             # Generate content using the Gemini API
-            response = model.generate_content(prompt)
-
-            # Ensure response contains text
+            response = genai.generate_content(
+                model='gemini-pro',
+                prompt=prompt,
+                temperature=0.1,  # Lower value for deterministic output
+                top_p=0.9,        # Wider range of tokens considered
+                max_output_tokens=500
+            )
+
+            # Validate and enforce format consistency
+            expected_format = r"(1\. \*\*Match Score:\*\* .+\n2\. \*\*Justification:\*\* .+\n3\. \*\*Resume Suggestions:\*\* .+\n4\. \*\*Interview Preparation Topics:\*\* .+)"
             if response and hasattr(response, "text"):
-                st.write(response.text)  # Display the generated response
+                output_text = response.text
+                if re.match(expected_format, output_text):
+                    st.write(output_text)  # Display the generated response
+                else:
+                    st.error("The response does not match the expected format. Please try again.")
             else:
                 st.error("No response received from the API.")