chat interface

app.py

@@ -8,6 +8,7 @@ from graph_handler import query_graph_qa, plot_subgraph
 from embed_handler import query_rag_qa
 from evaluate import evaluate_llm, reasoning_graph, get_coupon
 import base64
+import time
 
 load_dotenv()
 
@@ -44,65 +45,18 @@ def query_tqa(query, search_level):
     grag_response, grag_reference, grag_reference_text = query_graph_qa(
         graph_rag_index, query, search_level
     )
-    rag_response, rag_reference, rag_reference_text = query_rag_qa(
-        rag_index, query, search_level
-    )
+    # rag_response, rag_reference, rag_reference_text = query_rag_qa(
+    #     rag_index, query, search_level
+    # )
     return (
         grag_response,
-        grag_reference,
-        grag_reference_text,
-        rag_response,
-        rag_reference,
-        rag_reference_text,
+        # grag_reference,
+        # grag_reference_text,
+        # rag_response,
+        # rag_reference,
+        # 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.
@@ -127,126 +81,25 @@ 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
-
 with gr.Blocks() as demo:
     gr.Markdown("# Comfy Virtual Assistant")
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(label="Input Your Query")
+    clear = gr.ClearButton([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],
-    )
+    def respond(message, chat_history):
+            bot_message = query_tqa(message, 2)
+            chat_history.append((message, bot_message))
+            time.sleep(1)
+            return "", chat_history
+    
+    msg.submit(respond, [msg, chatbot], [msg, chatbot])
 
-    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():
-    #     reason_button = gr.Button("Get Graph Reasoning", variant="secondary")
-
-    # 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)