Update app.py

app.py

@@ -36,6 +36,8 @@ def extract_info_with_claude(resume_content: bytes) -> str:
         temp_file.write(resume_content)
         temp_file_path = temp_file.name
 
+    st.write("📄 Created temporary PDF file for Claude analysis")
+
     prompt = """
     Extract the following information from the given resume:
     1. Full Name
@@ -49,6 +51,8 @@ def extract_info_with_claude(resume_content: bytes) -> str:
     Extract all experiences, including projects, leadership, work experience, research, etc.
     """
     
+    st.write("🤖 Sending request to Claude API...")
+    
     try:
         message = anthropic.messages.create(
             model="claude-3-haiku-20240307",
@@ -69,23 +73,33 @@ def extract_info_with_claude(resume_content: bytes) -> str:
             }]
         )
         extracted_info = message.content[0].text
+        st.write("✅ Received response from Claude:")
+        st.code(extracted_info, language="text")
+        
     except Exception as e:
         extracted_info = f"An error occurred: {e}"
+        st.error(f"❌ API Error: {e}")
     finally:
         # Clean up the temporary file
         os.unlink(temp_file_path)
+        st.write("🗑️ Cleaned up temporary file")
     
     return extracted_info
 
 def parse_resume(uploaded_file: UploadedFile) -> Tuple[str, List[Dict]]:
     """Parse a resume file and return name and projects."""
     try:
+        st.write(f"📝 Processing resume: {uploaded_file.name}")
         resume_content = uploaded_file.getvalue()
+        st.write("📊 Extracted raw content from PDF")
+        
         extracted_info = extract_info_with_claude(resume_content)
+        st.write("🔍 Parsing extracted information...")
         
         # Parse the extracted information
         lines = extracted_info.split('\n')
         name = lines[0].split(': ')[1] if len(lines) > 0 and ': ' in lines[0] else "Unknown"
+        st.write(f"👤 Extracted name: {name}")
         
         projects = []
         project_started = False
@@ -100,6 +114,9 @@ def parse_resume(uploaded_file: UploadedFile) -> Tuple[str, List[Dict]]:
                     project_description = project_parts[1]
                     projects.append({"name": project_name, "description": project_description})
         
+        st.write("📋 Extracted projects:")
+        st.json(projects)
+        
         # Store in MongoDB
         resume_data = {
             "name": name,
@@ -107,11 +124,12 @@ def parse_resume(uploaded_file: UploadedFile) -> Tuple[str, List[Dict]]:
             "full_content": resume_content.decode('utf-8', errors='ignore')
         }
         resume_collection.insert_one(resume_data)
+        st.write("💾 Stored data in MongoDB")
         
         return name, projects
         
     except Exception as e:
-        st.error(f"Error processing resume: {e}")
+        st.error(f"❌ Error processing resume: {e}")
         return "Unknown", []
 
 def process_resumes(uploaded_files: List[UploadedFile]) -> Dict:
@@ -120,8 +138,10 @@ def process_resumes(uploaded_files: List[UploadedFile]) -> Dict:
     progress_bar = st.progress(0)
     
     for idx, file in enumerate(uploaded_files):
+        st.write(f"\n---\n### Processing file {idx + 1} of {len(uploaded_files)}")
+        
         if file.type != "application/pdf":
-            st.warning(f"Skipping {file.name}: Not a PDF file")
+            st.warning(f"⚠️ Skipping {file.name}: Not a PDF file")
             continue
             
         try:
@@ -132,8 +152,9 @@ def process_resumes(uploaded_files: List[UploadedFile]) -> Dict:
             }
             # Update progress
             progress_bar.progress((idx + 1) / len(uploaded_files))
+            st.write(f"✅ Successfully processed {file.name}")
         except Exception as e:
-            st.error(f"Error processing {file.name}: {e}")
+            st.error(f"❌ Error processing {file.name}: {e}")
                 
     return results
 
@@ -142,11 +163,19 @@ def display_results(results: Dict):
     if not results:
         return
         
-    st.subheader("Processed Resumes")
+    st.subheader("📊 Processed Resumes")
     
     for filename, data in results.items():
         with st.expander(f"📄 {data['name']} ({filename})"):
+            st.write("🏷️ File details:")
+            st.json({
+                "filename": filename,
+                "name": data['name'],
+                "number_of_projects": len(data['projects'])
+            })
+            
             if data['projects']:
+                st.write("📋 Projects:")
                 df = pd.DataFrame(data['projects'])
                 st.dataframe(
                     df,
@@ -157,13 +186,13 @@ def display_results(results: Dict):
                     hide_index=True
                 )
             else:
-                st.info("No projects found in this resume")
+                st.info("ℹ️ No projects found in this resume")
 
 def main():
-    st.title("IntraTalent Resume Processor")
+    st.title("🎯 IntraTalent Resume Processor")
     
     # File uploader section
-    st.header("Upload Resumes")
+    st.header("📤 Upload Resumes")
     uploaded_files = st.file_uploader(
         "Upload up to 10 resumes (PDF only)",
         type=['pdf'],
@@ -173,37 +202,40 @@ def main():
     
     # Validate number of files
     if uploaded_files and len(uploaded_files) > 10:
-        st.error("Maximum 10 files allowed. Please remove some files.")
+        st.error("⚠️ Maximum 10 files allowed. Please remove some files.")
         return
         
     # Process button
-    if uploaded_files and st.button("Process Resumes"):
+    if uploaded_files and st.button("🔄 Process Resumes"):
         with st.spinner("Processing resumes..."):
+            st.write("🚀 Starting resume processing...")
             results = process_resumes(uploaded_files)
             st.session_state['processed_results'] = results
+            st.write("✨ Processing complete!")
             display_results(results)
     
     # Query section
-    st.header("Search Projects")
+    st.header("🔍 Search Projects")
     query = st.text_area(
         "Enter your project requirements",
         placeholder="Example: Looking for team members with experience in machine learning and computer vision...",
         height=100
     )
     
-    if query and st.button("Search"):
+    if query and st.button("🔎 Search"):
         if 'processed_results' not in st.session_state:
-            st.warning("Please process some resumes first!")
+            st.warning("⚠️ Please process some resumes first!")
             return
             
         with st.spinner("Searching for matches..."):
+            st.write("🔄 Preparing to search...")
             # Here you would implement the embedding and similarity search
             # Using the code from your original script
-            st.success("Search completed!")
+            st.success("✅ Search completed!")
             # Display results in a nice format
-            st.subheader("Top Matches")
+            st.subheader("🎯 Top Matches")
             # Placeholder for search results
-            st.info("Feature coming soon: Will display matching projects and candidates based on similarity search")
+            st.info("🔜 Feature coming soon: Will display matching projects and candidates based on similarity search")
 
 if __name__ == "__main__":
     main()