Update app.py

app.py

@@ -16,34 +16,39 @@ def login(username, password):
         return False
 
 # Function to handle different API calls based on user input
-def handle_api_call(username, password, audio_file=None, pdf_file=None, message=None, query=None, question=None):
+def handle_api_call(username, password, message=None, client_name="rosariarossi", 
+                    system_prompt="You are an expert assistant", num_retrieved_docs=10, 
+                    num_docs_final=9, temperature=0, max_new_tokens=1024, 
+                    top_p=1, top_k=20, penalty=1.2, 
+                    pdf_file=None, query=None, question=None):
+    
     if not login(username, password):
         return "Invalid credentials! Please try again."
 
-    if audio_file:
-        # Handle audio file using the appropriate API
-        result = client.predict(audio=handle_file(audio_file), api_name="/process_audio")  # Example endpoint for audio processing
-        return result
-    elif pdf_file:
-        # Handle PDF file
-        pdf_result = client.predict(pdf_file=handle_file(pdf_file), client_name="rosariarossi", api_name="/process_pdf2")
-        return pdf_result[1]  # Returning the string result from the PDF processing
-    elif message:
+    if message:
         # Handle chat message
         chat_result = client.predict(
             message=message,
-            client_name="rosariarossi",
-            system_prompt="You are an expert assistant",
-            num_retrieved_docs=10,
-            num_docs_final=9,
-            temperature=0,
-            max_new_tokens=1024,
-            top_p=1,
-            top_k=20,
-            penalty=1.2,
+            client_name=client_name,
+            system_prompt=system_prompt,
+            num_retrieved_docs=num_retrieved_docs,
+            num_docs_final=num_docs_final,
+            temperature=temperature,
+            max_new_tokens=max_new_tokens,
+            top_p=top_p,
+            top_k=top_k,
+            penalty=penalty,
             api_name="/chat"
         )
         return chat_result
+    elif pdf_file:
+        # Handle PDF file
+        pdf_result = client.predict(
+            pdf_file=handle_file(pdf_file),
+            client_name=client_name,
+            api_name="/process_pdf2"
+        )
+        return pdf_result[1]  # Returning the string result from the PDF processing
     elif query:
         # Handle search query
         search_result = client.predict(query=query, api_name="/search_with_confidence")
@@ -63,21 +68,109 @@ with gr.Blocks() as app:
         username_input = gr.Textbox(label="Username", placeholder="Enter your username")
         password_input = gr.Textbox(label="Password", placeholder="Enter your password", type="password")
     
-    audio_input = gr.Audio(label="Upload Audio File", type="filepath")
-    pdf_input = gr.File(label="Upload PDF File")
-    
-    message_input = gr.Textbox(label="Enter Message for Chat")
-    query_input = gr.Textbox(label="Enter Search Query")
-    question_input = gr.Textbox(label="Enter Question for RAG")
+    with gr.Tab("Chat"):
+        message_input = gr.Textbox(label="Message", placeholder="Type your message here")
+        
+        gr.Markdown("### Client Options")
+        client_name_dropdown = gr.Dropdown(
+            label="Select Client",
+            choices=["rosariarossi", "bianchifiordaliso", "lorenzoverdi"],
+            value="rosariarossi"
+        )
+        
+        system_prompt_input = gr.Textbox(
+            label="System Prompt",
+            placeholder="Enter system prompt here",
+            value="You are an expert assistant"
+        )
+        
+        num_retrieved_docs_slider = gr.Slider(
+            label="Number of Initial Documents to Retrieve",
+            minimum=1,
+            maximum=100,
+            step=1,
+            value=10
+        )
+        
+        num_docs_final_slider = gr.Slider(
+            label="Number of Final Documents to Retrieve",
+            minimum=1,
+            maximum=100,
+            step=1,
+            value=9
+        )
+        
+        temperature_slider = gr.Slider(
+            label="Temperature",
+            minimum=0,
+            maximum=2,
+            step=0.1,
+            value=0
+        )
+        
+        max_new_tokens_slider = gr.Slider(
+            label="Max New Tokens",
+            minimum=1,
+            maximum=2048,
+            step=1,
+            value=1024
+        )
+        
+        top_p_slider = gr.Slider(
+            label="Top P",
+            minimum=0,
+            maximum=1,
+            step=0.01,
+            value=1
+        )
+        
+        top_k_slider = gr.Slider(
+            label="Top K",
+            minimum=1,
+            maximum=100,
+            step=1,
+            value=20
+        )
+        
+        penalty_slider = gr.Slider(
+            label="Repetition Penalty",
+            minimum=1,
+            maximum=5,
+            step=0.1,
+            value=1.2
+        )
+        
+        chat_output = gr.Textbox(label="Chat Response", interactive=False)
 
-    output_text = gr.Textbox(label="Output", interactive=False)
+    with gr.Tab("Process PDF"):
+        pdf_input = gr.File(label="Upload PDF File")
+        pdf_output = gr.Textbox(label="PDF Result", interactive=False)
+
+    with gr.Tab("Search"):
+        query_input = gr.Textbox(label="Enter Search Query")
+        search_output = gr.Textbox(label="Search Confidence Result", interactive=False)
+
+    with gr.Tab("Answer with RAG"):
+        question_input = gr.Textbox(label="Enter Question for RAG")
+        rag_output = gr.Textbox(label="RAG Answer Result", interactive=False)
 
-    # Bind the button click to the handle_api_call function
     api_button = gr.Button("Submit")
+
+    # Bind the button click to the handle_api_call function
     api_button.click(
         handle_api_call,
-        inputs=[username_input, password_input, audio_input, pdf_input, message_input, query_input, question_input],
-        outputs=output_text
+        inputs=[
+            username_input, password_input,
+            message_input, client_name_dropdown, 
+            system_prompt_input, num_retrieved_docs_slider, 
+            num_docs_final_slider, temperature_slider, 
+            max_new_tokens_slider, top_p_slider, 
+            top_k_slider, penalty_slider,
+            pdf_input, query_input, question_input
+        ],
+        outputs=[
+            chat_output, pdf_output, search_output, rag_output
+        ]
     )
 
 # Launch the app
@@ -87,6 +180,97 @@ app.launch()
 
 
 
+
+
+# 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 env variable
+
+# # Initialize the Gradio Client for the specified API
+# client = Client("on1onmangoes/CNIHUB10724v9", hf_token=HF_TOKEN)
+
+# # Authentication function
+# def login(username, password):
+#     if username == "your_username" and password == "your_password":  # Update with actual credentials
+#         return True
+#     else:
+#         return False
+
+# # Function to handle different API calls based on user input
+# def handle_api_call(username, password, audio_file=None, pdf_file=None, message=None, query=None, question=None):
+#     if not login(username, password):
+#         return "Invalid credentials! Please try again."
+
+#     if audio_file:
+#         # Handle audio file using the appropriate API
+#         result = client.predict(audio=handle_file(audio_file), api_name="/process_audio")  # Example endpoint for audio processing
+#         return result
+#     elif pdf_file:
+#         # Handle PDF file
+#         pdf_result = client.predict(pdf_file=handle_file(pdf_file), client_name="rosariarossi", api_name="/process_pdf2")
+#         return pdf_result[1]  # Returning the string result from the PDF processing
+#     elif message:
+#         # Handle chat message
+#         chat_result = client.predict(
+#             message=message,
+#             client_name="rosariarossi",
+#             system_prompt="You are an expert assistant",
+#             num_retrieved_docs=10,
+#             num_docs_final=9,
+#             temperature=0,
+#             max_new_tokens=1024,
+#             top_p=1,
+#             top_k=20,
+#             penalty=1.2,
+#             api_name="/chat"
+#         )
+#         return chat_result
+#     elif query:
+#         # Handle search query
+#         search_result = client.predict(query=query, api_name="/search_with_confidence")
+#         return search_result
+#     elif question:
+#         # Handle question for RAG
+#         rag_result = client.predict(question=question, api_name="/answer_with_rag")
+#         return rag_result
+#     else:
+#         return "No valid input provided!"
+
+# # Create the Gradio Blocks interface
+# with gr.Blocks() as app:
+#     gr.Markdown("### Login")
+    
+#     with gr.Row():
+#         username_input = gr.Textbox(label="Username", placeholder="Enter your username")
+#         password_input = gr.Textbox(label="Password", placeholder="Enter your password", type="password")
+    
+#     audio_input = gr.Audio(label="Upload Audio File", type="filepath")
+#     pdf_input = gr.File(label="Upload PDF File")
+    
+#     message_input = gr.Textbox(label="Enter Message for Chat")
+#     query_input = gr.Textbox(label="Enter Search Query")
+#     question_input = gr.Textbox(label="Enter Question for RAG")
+
+#     output_text = gr.Textbox(label="Output", interactive=False)
+
+#     # Bind the button click to the handle_api_call function
+#     api_button = gr.Button("Submit")
+#     api_button.click(
+#         handle_api_call,
+#         inputs=[username_input, password_input, audio_input, pdf_input, message_input, query_input, question_input],
+#         outputs=output_text
+#     )
+
+# # Launch the app
+# app.launch()
+
+
+
+
+
 # import gradio as gr
 
 # # Define a function for the main application