Update app.py

app.py

@@ -72,66 +72,72 @@ def query_vectara(question):
         verify=True,
         headers=api_key_header
     )
+
+    if response.status_code == 200:    
+        query_data = response.json()    
+        if query_data:    
+            sources_info = []    
+              
+            # Extract the summary.  
+            summary = query_data['responseSet'][0]['summary'][0]['text']  
   
-    if response.status_code == 200:  
-        query_data = response.json()  
-        print(query_data)  
-        if query_data:  
-            sources_info = []  
+            # Iterate over all response sets    
+            for response_set in query_data.get('responseSet', []):    
+                # Extract sources    
+                for source in response_set.get('response', [])[:5]:  # Limit to top 5 sources.  
+                    source_metadata = source.get('metadata', [])    
+                    source_info = {}    
+    
+                    for metadata in source_metadata:    
+                        metadata_name = metadata.get('name', '')    
+                        metadata_value = metadata.get('value', '')    
+    
+                        if metadata_name == 'title':    
+                            source_info['title'] = metadata_value    
+                        elif metadata_name == 'author':    
+                            source_info['author'] = metadata_value    
+                        elif metadata_name == 'pageNumber':    
+                            source_info['page number'] = metadata_value    
+    
+                    if source_info:    
+                        sources_info.append(source_info)    
               
-            # Iterate over all response sets  
-            for response_set in query_data.get('responseSet', []):  
-                # Extract sources  
-                for source in response_set.get('response', []):  
-                    source_metadata = source.get('metadata', [])  
-                    source_info = {}  
+            result = {"summary": summary, "sources": sources_info}  
+            return f"{json.dumps(result, indent=2)}"    
+        else:    
+            return "No data found in the response."    
+    else:    
+        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', [])  
   
-                    for metadata in source_metadata:  
-                        metadata_name = metadata.get('name', '')  
-                        metadata_value = metadata.get('value', '')  
+        # Format the summary as Markdown  
+        markdown_summary = f'**Summary:** {summary}\n\n'  
   
-                        if metadata_name == 'title':  
-                            source_info['title'] = metadata_value  
-                        elif metadata_name == 'author':  
-                            source_info['author'] = metadata_value  
-                        elif metadata_name == 'pageNumber':  
-                            source_info['page number'] = metadata_value  
+        # 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"  
   
-                    if source_info:  
-                        sources_info.append(source_info)  
-              
-            return f"Sources:\n{json.dumps(sources_info, indent=2)}"  
-        else:  
-            return "No data found in the response."  
+        return f"{markdown_summary}**Sources:**\n{markdown_sources}"  
     else:  
-        return f"Error: {response.status_code}"  
-
-
-def convert_to_markdown(vectara_response):
-    if vectara_response:
-        response_set = vectara_response.get('responseSet', [{}])[0]
-        summary = response_set.get('summary', [{}])[0]
-        summary_text = summary.get('text', 'No summary available')
-        sources = response_set.get('response', [])[:5]
-        sources_text = [source.get('text', '') for source in sources]
-
-        # Format the summary as Markdown
-        markdown_summary = f'**Summary:** {summary_text}\n\n'
-
-        # Format the sources as a numbered list
-        markdown_sources = "\n".join([f"{i+1}. {source}" for i, source in enumerate(sources_text)])
-
-        return f"{markdown_summary}**Sources:**\n{markdown_sources}"
-    else:
-        return "No data found in the response."
-
-
-iface = gr.Interface(
-    fn=query_vectara,
-    inputs=[gr.Textbox(label="Input Text")],
-    outputs=[gr.Markdown(label="Output Text")],
-    title="Vectara Chatbot",
-    description="Ask me anything using the Vectara API!"
-)
-
-iface.launch()
+        return "No data found in the response."  
+  
+  
+iface = gr.Interface(  
+    fn=lambda text: convert_to_markdown(query_vectara(text)),  
+    inputs=[gr.Textbox(label="Input Text")],  
+    outputs=[gr.Markdown(label="Output Text")],  
+    title="Vectara Chatbot",  
+    description="Ask me anything using the Vectara API!"  
+)  
+  
+iface.launch()