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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/CNIHUB10724v10", hf_token=HF_TOKEN) | |
| client = Client("on1onmangoes/CNIHUB101324v10", hf_token=HF_TOKEN) | |
| # on1onmangoes/CNIHUB101324v10 | |
| # Here's how you can fix it: | |
| # Update the conversation history within the function. | |
| # Return the updated history along with any other required outputs. | |
| def format_answer_with_documents(answer): | |
| """ | |
| This function formats the assistant's answer and separates the personal details from the relevant documents. | |
| """ | |
| # Extract the personal details and documents from the answer | |
| personal_details = answer[0] | |
| relevant_documents = answer[1:] # Assuming documents are included after the answer | |
| # Format the personal details | |
| formatted_answer = f"Based on the documents provided, the personal details are as follows:\n\n{personal_details}\n\n" | |
| # If there are relevant documents, format them as a separate section | |
| if relevant_documents: | |
| formatted_answer += "Relevant Documents:\n" | |
| for idx, document in enumerate(relevant_documents, start=1): | |
| formatted_answer += f"{idx}. {document.metadata['heading']} (Page {int(document.metadata['page_number'])})\n" | |
| formatted_answer += f" Source: {document.metadata['source']}\n" | |
| formatted_answer += f" Snippet: {document.page_content[:200]}...\n\n" # Showing a snippet of content | |
| return formatted_answer | |
| 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, | |
| ): | |
| print(f"Message: {message}") | |
| print(f"History: {history}") | |
| # Build the conversation prompt including system prompt and history | |
| conversation = f"{system_prompt}\n\nFor Client: {client_name}\n" | |
| # Add previous conversation history | |
| for user_input, assistant_response in history: | |
| conversation += f"User: {user_input}\nAssistant: {assistant_response}\n" | |
| # Add the current user message | |
| conversation += f"User: {message}\nAssistant:" | |
| # Call the API with the user's message | |
| question = message | |
| answer = client.predict(question=question, api_name="/answer_with_rag") | |
| # Debugging: Print the response | |
| print("The Answer in stream_chat_with_rag:") | |
| print(answer) | |
| # Format the assistant's answer to separate the relevant documents | |
| formatted_answer = format_answer_with_documents(answer) | |
| # Update the conversation history with the new message and answer | |
| history.append((message, formatted_answer)) | |
| # Return the formatted answer | |
| return formatted_answer | |
| # this version works just issue with formatting | |
| # 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, | |
| # ): | |
| # print(f"Message: {message}") | |
| # print(f"History: {history}") | |
| # # Build the conversation prompt including system prompt and history | |
| # conversation = system_prompt + "\n\n" + "For Client:" + client_name | |
| # for user_input, assistant_response in history: | |
| # conversation += f"User: {user_input}\nAssistant: {assistant_response}\n" | |
| # conversation += f"User: {message}\nAssistant:" | |
| # question = message | |
| # answer = client.predict(question=question, api_name="/answer_with_rag") | |
| # # debug 092624 | |
| # print("The Answer in stream_chat_with_rag:") | |
| # print(answer) | |
| # # Update the conversation history | |
| # history.append((message, answer)) | |
| # return answer | |
| # 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() | |