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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 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: str, | |
| history: list, | |
| client_name: str, | |
| system_prompt: str, | |
| num_retrieved_docs: int, | |
| num_docs_final: int, | |
| temperature: float, | |
| max_new_tokens: int, | |
| top_p: float, | |
| top_k: int, | |
| penalty: float, | |
| ): | |
| # Use the parameters provided by the UI | |
| 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 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( | |
| 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) | |
| ## 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 | |
| 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, 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() | |