Update app.py

app.py

@@ -5,46 +5,46 @@ import PyPDF2
 import io
 import re
 import streamlit as st
-from transformers import pipeline
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.metrics.pairwise import cosine_similarity
 
-# Configure API Key
+# Set API Key
 api_key = os.getenv("GOOGLE_API_KEY")
 if not api_key:
     st.error("API key not found. Please set GOOGLE_API_KEY in your environment variables.")
     st.stop()
 
+# Configure Generative AI client
 genai.configure(api_key=api_key)
 
-# Text Generation Function
-def generate_response(prompt, model="text-bison-001", max_output_tokens=256):
+# Generate Response using Gemini Flash 1.5
+def generate_with_gemini(prompt, model="gemini-1p5", max_output_tokens=256):
     """
-    Generate text response using Google Generative AI.
+    Generate a response using the Gemini Flash 1.5 model.
 
     Args:
-        prompt (str): Input prompt for AI.
-        model (str): Model to use for generation.
-        max_output_tokens (int): Maximum token limit.
+        prompt (str): Input prompt for the AI model.
+        model (str): Model to use (default: "gemini-1p5").
+        max_output_tokens (int): Limit for the generated output tokens.
 
     Returns:
-        str: Generated text or error message.
+        str: Generated text response from the model.
     """
     try:
-        response = genai.chat(
+        response = genai.generate_text(
             model=model,
-            messages=[{"role": "user", "content": prompt}],
+            prompt=prompt,
             temperature=0.7,
             max_output_tokens=max_output_tokens
         )
-        return response.result['content']
+        return response.result  # Adjust this if response structure differs
     except Exception as e:
         return f"Error generating text: {str(e)}"
 
-# PDF Text Extraction
+# Extract Text from Uploaded PDF
 def extract_text_from_pdf(file):
     """
-    Extract text from uploaded PDF.
+    Extract text from uploaded PDF file.
 
     Args:
         file (UploadedFile): PDF file uploaded via Streamlit.
@@ -77,7 +77,7 @@ def extract_contact_info(text):
     return (email.group(0) if email else "Not Available",
             phone.group(0) if phone else "Not Available")
 
-# Management Experience Extraction
+# Extract Management Experience
 def extract_management_experience(text):
     """
     Extract management and leadership keywords and years.
@@ -99,7 +99,7 @@ def extract_management_experience(text):
 
     return years, ", ".join(found_keywords) if found_keywords else "Not Available"
 
-# TF-IDF Match Percentage
+# Calculate Match Percentage
 def calculate_match_percentage(resume_text, job_description):
     """
     Calculate similarity between resume and job description using TF-IDF.
@@ -120,43 +120,63 @@ def calculate_match_percentage(resume_text, job_description):
         st.error(f"Error calculating match percentage: {str(e)}")
         return 0.0
 
-# Streamlit Interface
-st.title("Resume Analysis Tool: Management & Leadership Focus")
-st.markdown("### Upload Resume PDF and Enter Job Description")
+# Streamlit User Interface
+st.title("Resume ATS Analysis Tool: Powered by Gemini Flash 1.5")
+st.markdown("### Upload a Resume PDF and Enter a Job Description")
 
 uploaded_file = st.file_uploader("Upload Resume PDF", type=["pdf"])
 job_description = st.text_area("Job Description", height=200)
 
 if uploaded_file and job_description.strip():
     if st.button("Analyze"):
+        # Extract resume text
         resume_text = extract_text_from_pdf(uploaded_file)
         if not resume_text:
             st.error("Failed to extract text from PDF. Ensure the file is valid.")
             st.stop()
 
+        # Extract contact information
         email, phone = extract_contact_info(resume_text)
+
+        # Extract management experience
         management_years, management_keywords = extract_management_experience(resume_text)
+
+        # Calculate match percentage
         match_percentage = calculate_match_percentage(resume_text, job_description)
 
+        # Generate AI analysis
         prompt = f"""
         Analyze the resume with respect to the job description.
         Resume Text: {resume_text}
         Job Description: {job_description}
-        Include: Name, Skills, Education, Experience, and Match Percentage.
+        Provide details:
+        - Key Skills
+        - Education
+        - Management Experience (Years)
+        - Leadership Keywords
+        - Match Percentage
         """
-        gemini_response = generate_response(prompt)
+        gemini_response = generate_with_gemini(prompt)
 
+        # Display results
         results = {
             "Email": email,
             "Contact": phone,
             "Management Experience (Years)": management_years,
-            "Keywords": management_keywords,
+            "Leadership Keywords": management_keywords,
             "Match Percentage": match_percentage,
             "AI Summary": gemini_response
         }
 
         st.write(pd.DataFrame([results]))
+
+        # Allow CSV download
         csv = pd.DataFrame([results]).to_csv(index=False)
-        st.download_button("Download Results", data=csv, file_name="resume_analysis.csv", mime="text/csv")
+        st.download_button(
+            "Download Results",
+            data=csv,
+            file_name="resume_analysis_results.csv",
+            mime="text/csv"
+        )
 else:
-    st.info("Upload a resume and provide a job description to begin analysis.")
+    st.info("Please upload a resume and enter a job description to proceed.")