Upload app.py

app.py

@@ -21,7 +21,7 @@ load_dotenv()
 # Access Pinecone and OpenAI API keys from environment variables
 pinecone_api_key = os.getenv("PINECONE_API_KEY")
 openai.api_key = os.getenv("OPENAI_API_KEY")
-index_name = "amtrak-rmm-image-text"
+index_name = "amtrak-acela-ai-demo"
 
 # Initialize Pinecone using a class-based method
 pc = Pinecone(api_key=pinecone_api_key)
@@ -66,7 +66,7 @@ def get_model_response(human_input):
             image_url = flatten_to_string(result.get('metadata', {}).get('image_path', None))
             figure_desc = flatten_to_string(result.get('metadata', {}).get('figure_description', ''))
 
-            context_list.append(f"Document {ind+1}: {document_content}")
+            context_list.append(f"Relevant information: {document_content}")
             if image_url and figure_desc:
                 images.append((figure_desc, image_url))
 
@@ -108,6 +108,7 @@ def get_model_response(human_input):
 def get_model_response_with_images(human_input, history=None):
     output_text, images = get_model_response(human_input)
     if images:
+        # Append images in Markdown format for Gradio to render
         image_output = "".join([f"\n\n**{figure_desc}**\n![{figure_desc}]({image_path})" for figure_desc, image_path in images])
         return output_text + image_output
     return output_text