Update app.py

app.py

@@ -63,12 +63,6 @@ with gr.Blocks(css=CSS) as demo:
             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",
@@ -178,6 +172,186 @@ if __name__ == "__main__":
 
 
 
+# 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()
+
+
+
+
+
+
+
+
+