Update app.py

app.py

@@ -303,27 +303,6 @@ def query_vectara(text):
         return f"Error: {response.status_code}"
 
 
-def convert_to_markdown(vectara_response_json):
-    vectara_response = json.loads(vectara_response_json)
-    if vectara_response:
-        summary = vectara_response.get('summary', 'No summary available')
-        sources_info = vectara_response.get('sources', [])
-
-        # Format the summary as Markdown
-        markdown_summary = f' {summary}\n\n'
-
-        # Format the sources as a numbered list
-        markdown_sources = ""
-        for i, source_info in enumerate(sources_info):
-            author = source_info.get('author', 'Unknown author')
-            title = source_info.get('title', 'Unknown title')
-            page_number = source_info.get('page number', 'Unknown page number')
-            markdown_sources += f"{i+1}. {title} by {author}, Page {page_number}\n"
-
-        return f"{markdown_summary}**Sources:**\n{markdown_sources}"
-    else:
-        return "No data found in the response."
-
 # Functions to Wrap the Prompt Correctly
 def wrap_text(text, width=90):
     lines = text.split('\n')
@@ -425,20 +404,24 @@ def process_and_query(input_language=None, audio_input=None, image_input=None, t
         # Use the text to query Vectara
         vectara_response_json = query_vectara(combined_text)
         print("Vectara Response:", vectara_response_json)  # Debug print
-        
-        # Convert the Vectara response to Markdown
-        markdown_output = convert_to_markdown(vectara_response_json)
-        
-        # Append the original image description to the markdown output
+
+        # Parse the Vectara response
+        vectara_response = json.loads(vectara_response_json)
+        summary = vectara_response.get('summary', 'No summary available')
+        sources_info = vectara_response.get('sources', [])
+        print("Summary:", summary)  # Debug print
+        print("Sources Info:", sources_info)  # Debug print
+
+        # Append the original image description to the summary
         if image_description:
-            markdown_output += "\n\n**Original Image Description:**\n" + image_description
+            summary += "\n\n**Original Image Description:**\n" + image_description
         
         # Process the summary with OpenAI
-        final_response = process_summary_with_stablemed(markdown_output)
+        final_response = process_summary_with_stablemed(summary)
         print("Final Response:", final_response)  # Debug print
 
         # Evaluate hallucination
-        hallucination_label = evaluate_hallucination(final_response, markdown_output)
+        hallucination_label = evaluate_hallucination(final_response, summary)
         print("Hallucination Label:", hallucination_label)  # Debug print
         return final_response, hallucination_label
     except Exception as e: