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| import gradio as gr | |
| import langchain.llms as llms | |
| import langchain.llms.prompts as prompts | |
| import langchain.pipelines as pipelines | |
| from langchain.llms.responses import ResponseItem | |
| def process_pdf(pdf_file): | |
| """Processes the uploaded PDF using a pre-trained information extraction pipeline. | |
| Args: | |
| pdf_file (bytes): The uploaded PDF file content. | |
| Returns: | |
| dict: A dictionary containing the extracted information from the PDF. | |
| """ | |
| # Replace with your preferred information extraction pipeline | |
| # (e.g., Camelot, PyMuPDF, PDFMiner.Six) | |
| extracted_data = extract_information_from_pdf(pdf_file) | |
| return extracted_data | |
| def answer_question(question, context, llm): | |
| """Answers the user's question using the provided context and LLaMA3 model. | |
| Args: | |
| question (str): The user's question. | |
| context (dict): The extracted information from the PDF. | |
| llm (llms.BaseLLM): The LLaMA3 language model instance. | |
| Returns: | |
| ResponseItem: A ResponseItem object containing the answer, score, and retrieval. | |
| """ | |
| # Replace with your preferred RAG prompt template | |
| # (e.g., "The document says [RETRIEVAL]. Can you answer this question based on it: {question}?") | |
| prompt = prompts.get_rag_answering_prompt(question, context, retrieval_template="") | |
| response = llm.run(prompt, wait_for_sequences=True) | |
| return response[0] | |
| def chatbot(pdf_file, message, chat_history): | |
| """Handles user interaction, processes PDF, answers questions, and maintains chat history. | |
| Args: | |
| pdf_file (bytes, optional): The uploaded PDF file content (if applicable). | |
| message (str): The user's message (question). | |
| chat_history (list): A list of previous messages. | |
| Returns: | |
| list: An updated chat history with the chatbot's response. | |
| """ | |
| if pdf_file is not None: | |
| context = process_pdf(pdf_file) | |
| chat_history.append(f"**You uploaded a PDF.**") | |
| if message: | |
| # Access the LLaMA3 model (replace with your setup) | |
| llm = llms.get_llm("facebook/bart-base") # Example LLaMA3 model (replace with your access) | |
| response = answer_question(message, context, llm) | |
| chat_history.append(f"**User:** {message}") | |
| chat_history.append(f"**Chatbot:** {response.generated_text}") | |
| return chat_history | |
| # Gradio interface setup | |
| interface = gr.Interface( | |
| fn=chatbot, | |
| inputs=[ | |
| gr.File(type="pdf", label="Upload PDF (optional)"), | |
| gr.Textbox(label="Ask a question"), | |
| ], | |
| outputs=gr.Textbox(multiline=True), | |
| title="PDF Q&A Chatbot with LLaMA3", | |
| description="Ask questions about the uploaded PDF or provide an empty file to use example content.", | |
| ) | |
| interface.launch() |