Update app.py

app.py

@@ -57,6 +57,53 @@ def query_tqa(query, search_level):
         # rag_reference_text,
     )
 
+
+# def eval_llm(query, rag_response, grag_response):
+#     """
+#     Evaluate the Graph-RAG and RAG responses using an LLM.
+
+#     Args:
+#     query (str): The query that was asked.
+#     rag_response (str): The response from the Vanilla-RAG model.
+#     grag_response (str): The response from the Graph-RAG model.
+
+#     Returns:
+#     str: The evaluation text on various criteria from the LLM.
+#     """
+
+#     if not query.strip() or not rag_response.strip() or not grag_response.strip():
+#         raise gr.Error("Please ask a query and get responses before evaluating.")
+
+#     eval_text = evaluate_llm(query, grag_response, rag_response)
+#     return eval_text
+
+
+# def reason_and_plot(query, grag_response, grag_reference):
+#     """
+#     Get the reasoning graph for a query and plot the knowledge graph.
+
+#     Args:
+#     query (str): The query to ask the Graph-RAG.
+#     grag_response (str): The response from the Graph-RAG model.
+#     grag_reference (str): The reference text from the Graph-RAG model.
+
+#     Returns:
+#     tuple: The reasoning graph and the HTML to plot the knowledge graph.
+#     """
+
+#     if not query.strip() or not grag_response.strip() or not grag_reference.strip():
+#         raise gr.Error(
+#             "Please ask a query and get a Graph-RAG response before reasoning."
+#         )
+
+#     graph_reasoning = reasoning_graph(query, grag_response, grag_reference)
+#     escaped_html = plot_subgraph(grag_reference)
+
+#     iframe_html = f'<iframe srcdoc="{escaped_html}" width="100%" height="400px" frameborder="0"></iframe>'
+
+#     return graph_reasoning, iframe_html
+
+
 def show_graph():
     """
     Show the latest graph visualization in an iframe.
@@ -81,9 +128,45 @@ def show_graph():
         return f"Error: {str(e)}"
 
 
+def reveal_coupon(query, grag_response):
+    """
+    Get the coupon from the query and response.
+
+    Args:
+    query (str): Query asked to Graph-RAG.
+    grag_response (str): Response from the Graph-RAG model.
+
+    Returns:
+    str: Coupon with reasoning.
+    """
+
+    if not query.strip() or not grag_response.strip():
+        raise gr.Error("Please ask a query and get a response before revealing the coupon.")
+
+    coupon = get_coupon(query, grag_response)
+    return coupon
+# Implementing Gradio 5 features and building a ChatInterface UI yourself
+PLACEHOLDER = """<div style="padding: 20px; text-align: center; display: flex; flex-direction: column; align-items: center;">
+   <img src="https://ysharma-dummy-chat-app.hf.space/file=/tmp/gradio/c21ff9c8e7ecb2f7d957a72f2ef03c610ac7bbc4/Meta_lockup_positive%20primary_RGB_small.jpg" style="width: 80%; max-width: 550px; height: auto; opacity: 0.55; margin-bottom: 10px;">
+   <h1 style="font-size: 28px; margin: 0;">Meta llama3.2</h1>
+   <p style="font-size: 18px; margin: 5px 0 0; opacity: 0.65;">
+       <a href="https://huggingface.co/blog/llama32" target="_blank" style="color: inherit; text-decoration: none;">Learn more about Llama 3.2</a>
+   </p>
+</div>"""
+
+
 with gr.Blocks() as demo:
     gr.Markdown("# Comfy Virtual Assistant")
-    chatbot = gr.Chatbot()
+    chatbot = gr.Chatbot(
+        label="Comfy Virtual Assistant",
+        type="messages",
+        scale=1,
+        suggestions = [
+                {"text": "How much iphone cost?"},
+                {"text": "What phone options do i have ?"}
+                ],
+            placeholder = PLACEHOLDER,
+    )
     msg = gr.Textbox(label="Input Your Query")
     clear = gr.ClearButton([msg, chatbot])
 
@@ -94,12 +177,106 @@ with gr.Blocks() as demo:
             return "", chat_history
     
     msg.submit(respond, [msg, chatbot], [msg, chatbot])
+    
+    # with gr.Row():
+    #     with gr.Column(scale=4):
+    #         query_input = gr.Textbox(label="Input Your Query", lines=3)
+    #     # with gr.Column(scale=1):
+    #     #     search_level = gr.Slider(
+    #     #         minimum=1, maximum=50, value=3, step=5, label="Search Level"
+    #     #     )
+    #         ask_button = gr.Button("Ask Comfy", variant="primary")
+
+    # examples = gr.Examples(
+    #     examples=[
+    #         ["Recommend me an apple phone that has more than 10MP camera."],
+    #         ["What is the price of Samsung Galaxy S24 Ultra 12/256Gb Titanium Gray"],
+    #         ["I want a phone with 5000 mAH or more battery"],
+    #     ],
+    #     inputs=[query_input],
+    # )
+
+    # with gr.Row():
+    #     with gr.Column():
+    #         gr.Markdown("### Graph-RAG")
+    #         grag_output = gr.Textbox(label="Response", lines=5)
+            # grag_reference = gr.Textbox(label="Triplets", lines=3)
+            # with gr.Accordion("Extracted Reference (Raw)", open=False):
+            #     grag_reference_text = gr.Textbox(label="Raw Reference", lines=5)
+
+        # with gr.Column():
+        #     gr.Markdown("### Vanilla RAG")
+        #     rag_output = gr.Textbox(label="Response", lines=5)
+        #     rag_reference = gr.Textbox(label="Extracted Reference", lines=3)
+        #     with gr.Accordion("Extracted Reference (Raw)", open=False):
+        #         rag_reference_text = gr.Textbox(label="Raw Reference", lines=5)
+
+    # gr.Markdown("### Coupon")
+    # with gr.Row():    
+    #     with gr.Column():
+    #         coupon = gr.Text(label="Coupon", lines=1)
+    #     with gr.Column():
+    #         reveal = gr.Button("Reveal Coupon", variant="secondary")
+
+    # with gr.Row():
+    #     gr.Markdown("### Evaluate and Compare")
+
+    # with gr.Row():
+    #     eval_button = gr.Button("Evaluate LLMs", variant="secondary")
+
+    # grag_performance = gr.Textbox(label="Evaluation", lines=3)
 
+    # with gr.Row():
+    #     gr.Markdown("### Graph Reasoning")
 
-    with gr.Row():
-        plot_button = gr.Button("Plot Knowledge Graph", variant="secondary")
+    # with gr.Row():
+    #     reason_button = gr.Button("Get Graph Reasoning", variant="secondary")
 
-    kg_output = gr.HTML()
+    # with gr.Row():
+    #     with gr.Column():
+    #         grag_reasoning = gr.Textbox(label="Graph-RAG Reasoning", lines=5)
+    #     with gr.Column():
+    #         subgraph_plot = gr.HTML()
 
+    # with gr.Row():
+    #     plot_button = gr.Button("Plot Knowledge Graph", variant="secondary")
+
+    # kg_output = gr.HTML()
+
+    # ask_button.click(
+    #     query_tqa,
+    #     inputs=[query_input, search_level],
+    #     outputs=[
+    #         grag_output,
+    #         # grag_reference,
+    #         # grag_reference_text,
+    #         # rag_output,
+    #         # rag_reference,
+    #         # rag_reference_text,
+    #     ],
+    # )
+
+    # eval_button.click(
+    #     eval_llm,
+    #     inputs=[query_input, rag_output, grag_output],
+    #     outputs=[grag_performance],
+    # )
+
+    # reason_button.click(
+    #     reason_and_plot,
+    #     inputs=[query_input, grag_output, grag_reference],
+    #     outputs=[grag_reasoning, subgraph_plot],
+    # )
+
+    # plot_button.click(
+    #     show_graph,
+    #     outputs=[kg_output],
+    # )
+
+    # reveal.click(
+    #     reveal_coupon,
+    #     inputs=[query_input, grag_output],
+    #     outputs=[coupon],
+    # )
 
 demo.launch(auth=(os.getenv("ID"), os.getenv("PASS")), share=False)