Rename app (2).py to app.py

app (2).py

@@ -1,112 +0,0 @@
-import gradio as gr
-import os
-import agent
-# import comparison  
-from dotenv import load_dotenv
-
-# Load environment variables
-load_dotenv()
-
-# Initialize the RAG Agent for different election years
-
-agent = agent.RAGAgent(
-    api_key=os.getenv("PINECONE_API_KEY"),
-    # environment=os.getenv("PINECONE_ENVIRONMENT"),
-    index_name=os.getenv("PINECONE_INDEX") 
-)
-# agent_two = comparison.RAGAgent(
-#     api_key=os.getenv("PINECONE_API_KEY"),
-#     # environment=os.getenv("PINECONE_ENVIRONMENT"),
-#     index_name=os.getenv("PINECONE_INDEX") 
-# )
-def format_sources(sources):
-    """Format source nodes for display"""
-    if not sources:
-        return ""
-    
-    formatted = "Top Source Submissions/Comments:\n" + "-" * 50 + "\n"
-    for i, source in enumerate(sources, 1):
-        formatted += f"\n{i}. Text Content:\n{source['text']}\n"
-        formatted += f"Type: {source['metadata'].get('type', 'N/A')}\n"
-        formatted += f"Submission: {source['metadata'].get('post_title', 'N/A')}\n"
-        formatted += f"Created: {source['metadata'].get('created_time', 'N/A')}\n"
-        formatted += f"Score: {source['metadata'].get('score', 'N/A')}\n"
-        formatted += f"Year: {source['metadata'].get('year', 'N/A')}\n"
-        
-        formatted += "-" * 50 + "\n"
-    return formatted
-    
-def process_query(query, election_year):
-    """Process the query based on selected election year"""
-    # Select the appropriate agent
-    # agent = agent_2016 if election_year == "2016 Election" else agent_2024
-    if (election_year == "2016 Election") or (election_year == "2024 Election"):
-        # agent_select = agent_one
-        context = f"Looking for discussions about the election in {election_year}"
-        
-        result = agent.query(query, context)
-        response = f"Analysis for {election_year}:\n\n"
-        response += f"Answer: {result['answer']}\n\n"
-        response += format_sources(result['sources'])
-    else:
-        # agent_select = agent_two
-        context = f"Compare the discussions about the election between 2016 and 2024."
-        
-        result = agent.compare_with_context(query)
-        response = f"Analysis for {election_year}:\n\n"
-        response += f"Answer: {result['answer']}\n\n"
-        response += format_sources(result['sources'])
-        
-        
-    
-    # result = agent_select.query(query, context)
-
-    
-    return response
-
-# Create Gradio interface
-with gr.Blocks(title="Reddit Election Comments Analysis") as demo:
-    gr.Markdown("# Reddit Election Comments Analysis")
-    gr.Markdown("Ask questions about election-related comments and posts")
-    
-    with gr.Row():
-        with gr.Column():
-            # Add election year selector
-            year_selector = gr.Radio(
-                choices=["2016 Election", "2024 Election", "Comparison two years"],
-                label="Select Election Year",
-                value="2016 Election"  # Default value
-            )
-            
-            query_input = gr.Textbox(
-                label="Your Question",
-                placeholder="Ask about election comments or posts..."
-            )
-            # context_input = gr.Textbox(
-            #     label="Context (Optional)",
-            #     value = "Looking for discussions about the election results in 2016" #default value
-            # )
-            submit_btn = gr.Button("Submit")
-        
-        with gr.Column():
-            output = gr.Textbox(
-                label="Response",
-                lines=20
-            )
-    
-    # Update submit button to include year selection
-    submit_btn.click(
-        fn=process_query,
-        inputs=[query_input, year_selector],
-        outputs=output
-    )
-    
-    gr.Markdown("""
-    ## Example Questions:
-    - Is there any comments don't like the election results
-    - Summarize the main discussions about voting process
-    - What are the common opinions about candidates?
-     """)
-
-if __name__ == "__main__":
-    demo.launch(share=True)

app.py

@@ -0,0 +1,106 @@
+import gradio as gr
+from gradio_client import Client, handle_file
+import os
+
+
+# Define your Hugging Face token (make sure to set it as an environment variable)
+HF_TOKEN = os.getenv("HF_TOKEN")  # Replace with your actual token if not using an environment variable
+
+# Initialize the Gradio Client for the specified API
+client = Client("mangoesai/Elections_Comparing_Agent_V2", hf_token=HF_TOKEN)
+
+client_name = ['2016 Election','2024 Election', 'Comparison two years']
+
+
+
+def stream_chat_with_rag(
+    message: str,
+    history: list,
+    client_name: str
+):
+    print(f"Message: {message}")
+    print(f"History: {history}")
+
+    # Build the conversation prompt including system prompt and history
+    conversation = f"{system_prompt}\n\nFor Client: {client_name}\n"
+    
+    # Add previous conversation history
+    for user_input, assistant_response in history:
+        conversation += f"User: {user_input}\nAssistant: {assistant_response}\n"
+    
+    # Add the current user message
+    conversation += f"User: {message}\nAssistant:"
+
+    # Call the API with the user's process_query
+    question = message
+    #answer = client.predict(question=question, api_name="/run_graph")
+    answer = client.predict(
+    	query= message,
+		election_year=client_name,
+		api_name="/process_query"
+    )
+
+    # Debugging: Print the raw response
+    print("Raw answer from API:")
+    print(answer)
+
+
+    return answer
+
+
+    
+
+
+# Title for the application
+TITLE = "<h1 style='text-align:center;'>Reddit Election Q&A agent v0.1</h1>"
+
+# Create the Gradio Blocks interface
+with gr.Blocks(css=CSS) as demo:
+    gr.HTML(TITLE)
+    with gr.Tab("Chat"):
+        chatbot = gr.Chatbot()  # Create a chatbot interface
+        chat_interface = gr.ChatInterface(
+            fn=stream_chat_with_rag,
+            chatbot=chatbot,
+            additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
+            additional_inputs=[
+                gr.Dropdown(client_name,value="2016 Election",label="Select Election year", render=False,allow_custom_value=True)
+            ],
+        )
+
+
+    # with gr.Tab("Process PDF"):
+    #     pdf_input = gr.File(label="Upload PDF File")
+    #     #select_client_dropdown = gr.Dropdown(client_name, value="rosariarossi", label="Select or Type Client", allow_custom_value=True)
+    #     pdf_output = gr.Textbox(label="PDF Result", interactive=False)
+    
+    #     pdf_button = gr.Button("Process PDF")
+    #     pdf_button.click(
+    #         process_pdf,
+    #         inputs=[pdf_input],  # Pass both PDF and client name is not required 
+    #         outputs=pdf_output
+    #     )
+
+    # with gr.Tab("Answer with RAG"):
+    #     question_input = gr.Textbox(label="Enter Question for RAG")
+    #     answer_with_rag_select_client_dropdown = gr.Dropdown(client_name, value="primo", label="Select or Type Client", allow_custom_value=True)
+    #     rag_output = gr.Textbox(label="RAG Answer Result", interactive=False)
+
+    #     rag_button = gr.Button("Get Answer")
+    #     rag_button.click(
+    #         rag_api,
+    #         inputs=[question_input,answer_with_rag_select_client_dropdown ],
+    #         outputs=rag_output
+    #     )
+    # with gr.Tab(label="Manage Files"):
+    #     with gr.Column():
+    #         delete_index_button = gr.Button("Delete All Files")
+    #         delete_index_textout = gr.Textbox(label="Deleted Files and Refresh Result")
+    #         delete_index_button.click(fn=delete_index, inputs=[],outputs=[delete_index_textout])
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()
+
+
+