Update app.py

app.py

@@ -6,7 +6,7 @@ import os
 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("on1onmangoes/CNIHUB10724v9", hf_token=HF_TOKEN)
+client = Client("on1onmangoes/CNIHUB10724v10", hf_token=HF_TOKEN)
  
 
 # Function to handle chat API call
@@ -40,58 +40,6 @@ def stream_chat_with_rag(
     # Return the assistant's reply
     return response
 
-
-
-
-# # OG code in V9
-# def stream_chat_with_rag(
-#     message: str,
-#     history: list,
-#     client_name: str,
-#     system_prompt: str,
-#     num_retrieved_docs: int = 10,
-#     num_docs_final: int = 9,
-#     temperature: float = 0, 
-#     max_new_tokens: int = 1024, 
-#     top_p: float = 1.0, 
-#     top_k: int = 20, 
-#     penalty: float = 1.2,
-# ):
-
-# # Function to handle chat API call
-# # def stream_chat_with_rag(message, system_prompt, num_retrieved_docs, num_docs_final, temperature, max_new_tokens, top_p, top_k, penalty):
-# #     response = client.predict(
-# #         message=message,
-# #         client_name="rosariarossi",  # Hardcoded 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 response
-
-#     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 result
-
-
-
 # Function to handle PDF processing API call
 def process_pdf(pdf_file):
     return client.predict(
@@ -121,84 +69,6 @@ TITLE = "<h1 style='text-align:center;'>My Gradio Chat App</h1>"
 # Create the Gradio Blocks interface
 with gr.Blocks(css=CSS) as demo:
     gr.HTML(TITLE)
-    ## OG v9 comments 
-    #  gr.ChatInterface(
-    #     fn=stream_chat_with_rag,
-    #     chatbot=chatbot,
-    #     fill_height=True,
-    #     #gr.dropdown(['rosariarossi','bianchifiordaliso','lorenzoverdi'],label="Select Client"),
-    #     additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
-    #     additional_inputs=[
-    #         gr.Dropdown(['rosariarossi','bianchifiordaliso','lorenzoverdi'],value="rosariarossi",label="Select Client", render=False,),
-    #         gr.Textbox(
-    #             # value="""Using the information contained in the context,
-    #             #      give a comprehensive answer to the question.
-    #             #     Respond only to the question asked, response should be concise and relevant to the question.
-    #             #     Provide the number of the source document when relevant.
-    #             #     If the answer cannot be deduced from the context, do not give an answer""",
-    #             value ="""You are an expert assistant""",
-    #             label="System Prompt",
-    #             render=False,
-    #         ),
-    #         gr.Slider(
-    #             minimum=1,
-    #             maximum=10,
-    #             step=1,
-    #             value=10,
-    #             label="Number of Initial Documents to Retrieve",
-    #             render=False,
-    #         ),
-    #         gr.Slider(
-    #             minimum=1,
-    #             maximum=10,
-    #             step=1,
-    #             value=9,
-    #             label="Number of Final Documents to Retrieve",
-    #             render=False,
-    #         ),
-    #         gr.Slider(
-    #             minimum=0.2,
-    #             maximum=1,
-    #             step=0.1,
-    #             value=0,
-    #             label="Temperature",
-    #             render=False,
-    #         ),
-    #         gr.Slider(
-    #             minimum=128,
-    #             maximum=8192,
-    #             step=1,
-    #             value=1024,
-    #             label="Max new tokens",
-    #             render=False,
-    #         ),
-    #         gr.Slider(
-    #             minimum=0.0,
-    #             maximum=1.0,
-    #             step=0.1,
-    #             value=1.0,
-    #             label="top_p",
-    #             render=False,
-    #         ),
-    #         gr.Slider(
-    #             minimum=1,
-    #             maximum=20,
-    #             step=1,
-    #             value=20,
-    #             label="top_k",
-    #             render=False,
-    #         ),
-    #         gr.Slider(
-    #             minimum=0.0,
-    #             maximum=2.0,
-    #             step=0.1,
-    #             value=1.2,
-    #             label="Repetition penalty",
-    #             render=False,
-    #         ),
-    #     ],
-        
-    # )
     with gr.Tab("Chat"):
         chatbot = gr.Chatbot()  # Create a chatbot interface
 
@@ -310,681 +180,3 @@ if __name__ == "__main__":
     demo.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 an environment variable
-
-# # Initialize the Gradio Client for the specified API
-# client = Client("on1onmangoes/CNIHUB10724v9", hf_token=HF_TOKEN)
-
-# # Function to handle chat API call
-# def stream_chat_with_rag(message, system_prompt, num_retrieved_docs, num_docs_final, temperature, max_new_tokens, top_p, top_k, penalty):
-#     response = client.predict(
-#         message=message,
-#         client_name="rosariarossi",  # Hardcoded 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 response
-
-# # Function to handle PDF processing API call
-# def process_pdf(pdf_file):
-#     return client.predict(
-#         pdf_file=handle_file(pdf_file),
-#         client_name="rosariarossi",  # Hardcoded client name
-#         api_name="/process_pdf2"
-#     )[1]  # Return only the result string
-
-# # Function to handle search API call
-# def search_api(query):
-#     return client.predict(query=query, api_name="/search_with_confidence")
-
-# # Function to handle RAG API call
-# def rag_api(question):
-#     return client.predict(question=question, api_name="/answer_with_rag")
-
-# # CSS for custom styling
-# CSS = """
-# # chat-container {
-#     height: 100vh;
-# }
-# """
-
-# # Title for the application
-# TITLE = "<h1 style='text-align:center;'>My Gradio Chat App</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(
-#                     ['rosariarossi', 'bianchifiordaliso', 'lorenzoverdi'],
-#                     value="rosariarossi",
-#                     label="Select Client",
-#                     render=False,
-#                 ),
-#                 gr.Textbox(
-#                     value="You are an expert assistant",
-#                     label="System Prompt",
-#                     render=False,
-#                 ),
-#                 gr.Slider(
-#                     minimum=1,
-#                     maximum=10,
-#                     step=1,
-#                     value=10,
-#                     label="Number of Initial Documents to Retrieve",
-#                     render=False,
-#                 ),
-#                 gr.Slider(
-#                     minimum=1,
-#                     maximum=10,
-#                     step=1,
-#                     value=9,
-#                     label="Number of Final Documents to Retrieve",
-#                     render=False,
-#                 ),
-#                 gr.Slider(
-#                     minimum=0.2,
-#                     maximum=1,
-#                     step=0.1,
-#                     value=0,
-#                     label="Temperature",
-#                     render=False,
-#                 ),
-#                 gr.Slider(
-#                     minimum=128,
-#                     maximum=8192,
-#                     step=1,
-#                     value=1024,
-#                     label="Max new tokens",
-#                     render=False,
-#                 ),
-#                 gr.Slider(
-#                     minimum=0.0,
-#                     maximum=1.0,
-#                     step=0.1,
-#                     value=1.0,
-#                     label="Top P",
-#                     render=False,
-#                 ),
-#                 gr.Slider(
-#                     minimum=1,
-#                     maximum=20,
-#                     step=1,
-#                     value=20,
-#                     label="Top K",
-#                     render=False,
-#                 ),
-#                 gr.Slider(
-#                     minimum=0.0,
-#                     maximum=2.0,
-#                     step=0.1,
-#                     value=1.2,
-#                     label="Repetition Penalty",
-#                     render=False,
-#                 ),
-#             ],
-#         )
-
-#     with gr.Tab("Process PDF"):
-#         pdf_input = gr.File(label="Upload PDF File")
-#         pdf_output = gr.Textbox(label="PDF Result", interactive=False)
-
-#         pdf_button = gr.Button("Process PDF")
-#         pdf_button.click(
-#             process_pdf,
-#             inputs=[pdf_input],
-#             outputs=pdf_output
-#         )
-
-#     with gr.Tab("Search"):
-#         query_input = gr.Textbox(label="Enter Search Query")
-#         search_output = gr.Textbox(label="Search Confidence Result", interactive=False)
-
-#         search_button = gr.Button("Search")
-#         search_button.click(
-#             search_api,
-#             inputs=query_input,
-#             outputs=search_output
-#         )
-
-#     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)
-
-#         rag_button = gr.Button("Get Answer")
-#         rag_button.click(
-#             rag_api,
-#             inputs=question_input,
-#             outputs=rag_output
-#         )
-
-# # Launch the app
-# if __name__ == "__main__":
-#     demo.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 an environment variable
-
-# # Initialize the Gradio Client for the specified API
-# client = Client("on1onmangoes/CNIHUB10724v9", hf_token=HF_TOKEN)
-
-# # Function to handle chat API call
-# def stream_chat_with_rag(message, client_name, system_prompt, num_retrieved_docs, num_docs_final, temperature, max_new_tokens, top_p, top_k, penalty):
-#     response = client.predict(
-#         message=message,
-#         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 response
-
-# # Function to handle PDF processing API call
-# def process_pdf(pdf_file, client_name):
-#     return client.predict(
-#         pdf_file=handle_file(pdf_file),
-#         client_name=client_name,
-#         api_name="/process_pdf2"
-#     )[1]  # Return only the result string
-
-# # Function to handle search API call
-# def search_api(query):
-#     return client.predict(query=query, api_name="/search_with_confidence")
-
-# # Function to handle RAG API call
-# def rag_api(question):
-#     return client.predict(question=question, api_name="/answer_with_rag")
-
-# # 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")
-    
-#     with gr.Tab("Chat"):
-#         chatbot = gr.Chatbot()  # Create a chatbot interface
-
-#         chat_interface = gr.ChatInterface(
-#             fn=stream_chat_with_rag,
-#             chatbot=chatbot,
-#             fill_height=True,
-#             additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
-#             additional_inputs=[
-#                 gr.Dropdown(
-#                     ['rosariarossi', 'bianchifiordaliso', 'lorenzoverdi'],
-#                     value="rosariarossi",
-#                     label="Select Client",
-#                     render=False,
-#                 ),
-#                 gr.Textbox(
-#                     value="You are an expert assistant",
-#                     label="System Prompt",
-#                     render=False,
-#                 ),
-#                 gr.Slider(
-#                     minimum=1,
-#                     maximum=10,
-#                     step=1,
-#                     value=10,
-#                     label="Number of Initial Documents to Retrieve",
-#                     render=False,
-#                 ),
-#                 gr.Slider(
-#                     minimum=1,
-#                     maximum=10,
-#                     step=1,
-#                     value=9,
-#                     label="Number of Final Documents to Retrieve",
-#                     render=False,
-#                 ),
-#                 gr.Slider(
-#                     minimum=0.2,
-#                     maximum=1,
-#                     step=0.1,
-#                     value=0,
-#                     label="Temperature",
-#                     render=False,
-#                 ),
-#                 gr.Slider(
-#                     minimum=128,
-#                     maximum=8192,
-#                     step=1,
-#                     value=1024,
-#                     label="Max new tokens",
-#                     render=False,
-#                 ),
-#                 gr.Slider(
-#                     minimum=0.0,
-#                     maximum=1.0,
-#                     step=0.1,
-#                     value=1.0,
-#                     label="Top P",
-#                     render=False,
-#                 ),
-#                 gr.Slider(
-#                     minimum=1,
-#                     maximum=20,
-#                     step=1,
-#                     value=20,
-#                     label="Top K",
-#                     render=False,
-#                 ),
-#                 gr.Slider(
-#                     minimum=0.0,
-#                     maximum=2.0,
-#                     step=0.1,
-#                     value=1.2,
-#                     label="Repetition Penalty",
-#                     render=False,
-#                 ),
-#             ],
-#         )
-
-#     with gr.Tab("Process PDF"):
-#         pdf_input = gr.File(label="Upload PDF File")
-#         pdf_output = gr.Textbox(label="PDF Result", interactive=False)
-
-#         pdf_button = gr.Button("Process PDF")
-#         pdf_button.click(
-#             process_pdf,
-#             inputs=[pdf_input, client_name_dropdown],
-#             outputs=pdf_output
-#         )
-
-#     with gr.Tab("Search"):
-#         query_input = gr.Textbox(label="Enter Search Query")
-#         search_output = gr.Textbox(label="Search Confidence Result", interactive=False)
-
-#         search_button = gr.Button("Search")
-#         search_button.click(
-#             search_api,
-#             inputs=query_input,
-#             outputs=search_output
-#         )
-
-#     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)
-
-#         rag_button = gr.Button("Get Answer")
-#         rag_button.click(
-#             rag_api,
-#             inputs=question_input,
-#             outputs=rag_output
-#         )
-
-# # Launch the app
-# 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, 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 message:
-#         # Handle chat message
-#         chat_result = client.predict(
-#             message=message,
-#             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")
-#         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")
-    
-#     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)
-
-#     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)
-
-#     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,
-#             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
-# 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
-# def greet(name):
-#     return f"Hello {name}!"
-
-# # Define a function for the authentication
-# def login(username, password):
-#     if username == "your_username" and password == "your_password":
-#         return True
-#     else:
-#         return False
-
-# # 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")
-    
-#     login_button = gr.Button("Login")
-#     output_text = gr.Textbox(label="Output", interactive=False)
-
-#     # Function to handle login and display greeting
-#     def handle_login(username, password):
-#         if login(username, password):
-#             # Clear the password field and display the greeting
-#             #password_input.clear()
-#             return greet(username)
-#         else:
-#             return "Invalid credentials! Please try again."
-    
-#     # Bind the button click to the handle_login function
-#     login_button.click(handle_login, inputs=[username_input, password_input], outputs=output_text)
-
-# # Launch the app
-# app.launch()
-
-