Fixed app v2

app.py

@@ -1,5 +1,6 @@
 import gradio as gr
 from llama_cpp import Llama
+from transformers import pipeline
 import json
 
 # Load the Llama model
@@ -11,24 +12,38 @@ try:
 except Exception as e:
     raise RuntimeError(f"Failed to load model: {e}")
 
+# Load summarization model
+summarizer = pipeline("summarization")
+
+# Summarize text to fit within token limits
+def summarize_text(text, max_length=100):
+    # Use the summarizer to condense the text
+    summary = summarizer(text, max_length=max_length, min_length=25, do_sample=False)
+    return summary[0]["summary_text"]
+
 # Function to match CV to job descriptions with debug information
 def match_cv_to_jobs(cv_text, job_descriptions):
     debug_info = "Debug Info:\n"
     results = []
     
-    try:
-        # Split job descriptions by line
-        descriptions = job_descriptions.strip().split("\n")
+    # Summarize `cv_text` and `job_descriptions` to manage token limits
+    summarized_cv = summarize_text(cv_text, max_length=400)
+    debug_info += f"Summarized CV Text: {summarized_cv}\n"
+    
+    descriptions = job_descriptions.strip().split("\n")
+    for description in descriptions:
+        summarized_description = summarize_text(description, max_length=100)
+        debug_info += f"\nSummarized Job Description: {summarized_description}\n"
         
-        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
+        # Create a prompt to compare the summarized CV with each summarized job description
+        prompt = (
+            f"Compare the following job description with this resume. Job Description: {summarized_description}. "
+            f"Resume: {summarized_cv}. Provide a match score and a brief analysis."
+        )
+        debug_info += f"\nGenerated Prompt: {prompt}\n"
+        
+        # Generate response from the model
+        try:
             response = llm.create_chat_completion(
                 messages=[
                     {
@@ -49,17 +64,17 @@ def match_cv_to_jobs(cv_text, job_descriptions):
             except json.JSONDecodeError:
                 results.append({
                     "Job Description": description,
-                    "Analysis": response_content  # Use raw response if JSON parsing fails
+                    "Analysis": response_content
                 })
-    except Exception as e:
-        debug_info += f"Error: {str(e)}\n"
-        results.append({"Error": str(e)})
+        except Exception as e:
+            debug_info += f"Error: {str(e)}\n"
+            results.append({"Job Description": description, "Error": str(e)})
     
     return results, debug_info
 
 # Gradio interface
 with gr.Blocks() as demo:
-    gr.Markdown("# CV and Job Description Matcher with Debugging")
+    gr.Markdown("# CV and Job Description Matcher with Summarization and Debugging")
     
     # Input fields for CV and job descriptions
     cv_text = gr.Textbox(label="CV Text", placeholder="Enter the CV text here", lines=10)