Fixed app v2

app.py

@@ -11,47 +11,55 @@ try:
 except Exception as e:
     raise RuntimeError(f"Failed to load model: {e}")
 
-# Function to match CV to job descriptions
+# Function to match CV to job descriptions with debug information
 def match_cv_to_jobs(cv_text, job_descriptions):
-    # Split job descriptions by line
-    descriptions = job_descriptions.strip().split("\n")
+    debug_info = "Debug Info:\n"
     results = []
     
-    for description in descriptions:
-        # Create a prompt to compare the CV with each job description
-        prompt = (
-            f"Compare the following job description with this resume. Job Description: {description}. "
-            f"Resume: {cv_text}. Provide a match score and a brief analysis."
-        )
+    try:
+        # Split job descriptions by line
+        descriptions = job_descriptions.strip().split("\n")
         
-        # Generate response from the model
-        response = llm.create_chat_completion(
-            messages=[
-                {
-                    "role": "user",
-                    "content": prompt
-                }
-            ]
-        )
-        
-        # Extract the analysis text
-        analysis_text = response["choices"][0]["message"]["content"]
-        
-        # Attempt to parse as JSON; if not JSON, use the raw text
-        try:
-            response_data = json.loads(analysis_text)
-            results.append(response_data)
-        except json.JSONDecodeError:
-            results.append({
-                "Job Description": description,
-                "Analysis": analysis_text  # Use raw response if JSON parsing fails
-            })
+        for description in descriptions:
+            # Create a prompt to compare the CV with each job description
+            prompt = (
+                f"Compare the following job description with this resume. Job Description: {description}. "
+                f"Resume: {cv_text}. Provide a match score and a brief analysis."
+            )
+            debug_info += f"\nGenerated Prompt: {prompt}\n"
+            
+            # Generate response from the model
+            response = llm.create_chat_completion(
+                messages=[
+                    {
+                        "role": "user",
+                        "content": prompt
+                    }
+                ]
+            )
+            
+            # Extract the analysis text
+            response_content = response["choices"][0]["message"]["content"]
+            debug_info += f"Model Response: {response_content}\n"
+            
+            # Attempt to parse as JSON; if not JSON, use the raw text
+            try:
+                response_data = json.loads(response_content)
+                results.append(response_data)
+            except json.JSONDecodeError:
+                results.append({
+                    "Job Description": description,
+                    "Analysis": response_content  # Use raw response if JSON parsing fails
+                })
+    except Exception as e:
+        debug_info += f"Error: {str(e)}\n"
+        results.append({"Error": str(e)})
     
-    return results
+    return results, debug_info
 
 # Gradio interface
 with gr.Blocks() as demo:
-    gr.Markdown("# CV and Job Description Matcher")
+    gr.Markdown("# CV and Job Description Matcher with Debugging")
     
     # Input fields for CV and job descriptions
     cv_text = gr.Textbox(label="CV Text", placeholder="Enter the CV text here", lines=10)
@@ -60,8 +68,9 @@ with gr.Blocks() as demo:
     # Button and output area
     match_button = gr.Button("Match CV to Job Descriptions")
     output = gr.JSON(label="Match Results")
+    debug_output = gr.Textbox(label="Debug Info", lines=10)  # Add a debug box to display debug info
     
     # Set button click to run the function
-    match_button.click(fn=match_cv_to_jobs, inputs=[cv_text, job_descriptions], outputs=output)
+    match_button.click(fn=match_cv_to_jobs, inputs=[cv_text, job_descriptions], outputs=[output, debug_output])
 
 demo.launch()