Update app.py

app.py

@@ -7,6 +7,7 @@ import re
 import google.generativeai as genai
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.metrics.pairwise import cosine_similarity
+import pandas as pd
 import json
 
 # Load pre-trained embedding model for basic analysis
@@ -80,11 +81,11 @@ def analyze_with_gemini(resume_text, job_desc):
         # Handle parsing errors
         return {
             "Candidate Name": "Unknown",
-            "Relevant Skills": "Failed to extract",
-            "Educational Background": "Failed to extract",
-            "Leadership Experience (years)": "Failed to extract",
-            "Management Experience (years)": "Failed to extract",
-            "Management Skills": "Failed to extract",
+            "Relevant Skills": "Failed",
+            "Educational Background": "Failed",
+            "Leadership Experience (years)": "Failed",
+            "Management Experience (years)": "Failed",
+            "Management Skills": "Failed",
             "Summary": response.text.strip()
         }
 
@@ -161,25 +162,20 @@ def process_resumes(job_desc_file, resumes):
             "Gemini Analysis": gemini_analysis["Summary"]
         })
 
-    # Format results for display
-    output = "\n\n".join(
-        f"**{res['Resume']}**\n"
-        f"Overall Score: {res['Overall Score']}%\n"
-        f"Similarity (Embed): {res['Similarity (Embed)']}%\n"
-        f"Match Percentage (TF-IDF): {res['Match Percentage (TF-IDF)']}%\n"
-        f"Leadership Experience (years): {res['Leadership Experience (years)']} years\n"
-        f"Management Experience (years): {res['Management Experience (years)']} years\n"
-        f"Management Skills (combined): {res['Management Skills']}\n"
-        f"Gemini Analysis:\n{res['Gemini Analysis']}\n"
-        for res in results
-    )
-    return output
+    # Convert results to pandas DataFrame
+    df = pd.DataFrame(results)
+
+    # Save DataFrame to CSV
+    csv_file = "/tmp/analysis_results.csv"
+    df.to_csv(csv_file, index=False)
+
+    return csv_file
 
 # Gradio Interface
 job_desc_input = gr.File(label="Upload Job Description (TXT, PDF, DOCX)", type="filepath")
 resumes_input = gr.Files(label="Upload Resumes (TXT, PDF, DOCX)", type="filepath")
 
-results_output = gr.Textbox(label="Analysis Results", lines=30)
+results_output = gr.File(label="Download Analysis Results (CSV)")
 
 interface = gr.Interface(
     fn=process_resumes,