Spaces:
Sleeping
Sleeping
| 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() | |