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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()
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