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| # import os | |
| # import gradio as gr | |
| # from langchain_community.vectorstores import FAISS | |
| # from langchain_community.embeddings import HuggingFaceEmbeddings | |
| # from langchain_community.document_loaders import PyPDFLoader | |
| # from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| # from langchain.chains import ConversationalRetrievalChain | |
| # from langchain.memory import ConversationBufferMemory | |
| # from langchain_huggingface import HuggingFaceEndpoint | |
| # # Set token for Hugging Face Inference API | |
| # os.environ["HUGGINGFACEHUB_API_TOKEN"] = os.getenv("HUGGINGFACEHUB_API_TOKEN") | |
| # # Load embedding model | |
| # embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2") | |
| # # Initialize LLM | |
| # llm = HuggingFaceEndpoint( | |
| # repo_id="mistralai/Mistral-7B-Instruct-v0.3", | |
| # task="text-generation", | |
| # max_new_tokens=128, | |
| # temperature=0.5, | |
| # ) | |
| # # Memory for conversation context | |
| # memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) | |
| # # Shared state | |
| # qa_chain = None | |
| # # Step 1: Upload + Process PDF | |
| # def process_pdf(pdf_file): | |
| # global qa_chain | |
| # loader = PyPDFLoader(pdf_file.name) | |
| # pages = loader.load() | |
| # splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50) | |
| # chunks = splitter.split_documents(pages) | |
| # vectordb = FAISS.from_documents(chunks, embeddings) | |
| # qa_chain = ConversationalRetrievalChain.from_llm( | |
| # llm=llm, | |
| # retriever=vectordb.as_retriever(), | |
| # memory=memory | |
| # ) | |
| # return "PDF processed! You can now ask questions." | |
| # # Step 2: Chatbot logic | |
| # def chat_fn(message, history=[]): | |
| # if qa_chain is None: | |
| # return history + [[message, "⚠️ Please upload a PDF first."]], history | |
| # answer = qa_chain.run(message) | |
| # history.append((message, answer)) | |
| # return history, history | |
| # # Gradio UI | |
| # with gr.Blocks() as demo: | |
| # gr.Markdown("## 📄 Chat with PDF using Mistral 7B + LangChain") | |
| # upload = gr.File(label="Upload a PDF", type="filepath") | |
| # status = gr.Textbox(label="Status", interactive=False) | |
| # upload.change(fn=process_pdf, inputs=upload, outputs=status) | |
| # gr.ChatInterface(chat_fn) | |
| # demo.launch() | |
| # import os | |
| # import gradio as gr | |
| # from langchain_community.vectorstores import FAISS | |
| # from langchain_community.embeddings import HuggingFaceEmbeddings | |
| # from langchain_community.document_loaders import PyPDFLoader | |
| # from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| # from langchain.chains import ConversationalRetrievalChain | |
| # from langchain.memory import ConversationBufferMemory | |
| # from langchain_huggingface import HuggingFaceEndpoint | |
| # # Set token for Hugging Face Inference API | |
| # os.environ["HUGGINGFACEHUB_API_TOKEN"] = os.getenv("HUGGINGFACEHUB_API_TOKEN") | |
| # # Load embedding model | |
| # embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2") | |
| # # Initialize LLM | |
| # llm = HuggingFaceEndpoint( | |
| # repo_id="mistralai/Mistral-7B-Instruct-v0.3", | |
| # task="text-generation", | |
| # max_new_tokens=128, | |
| # temperature=0.5, | |
| # ) | |
| # # Memory for conversation context | |
| # memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) | |
| # # Shared state | |
| # qa_chain = None | |
| # # Step 1: Upload + Process PDF | |
| # def process_pdf(pdf_file): | |
| # global qa_chain | |
| # loader = PyPDFLoader(pdf_file.name) | |
| # pages = loader.load() | |
| # splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50) | |
| # chunks = splitter.split_documents(pages) | |
| # vectordb = FAISS.from_documents(chunks, embeddings) | |
| # qa_chain = ConversationalRetrievalChain.from_llm( | |
| # llm=llm, | |
| # retriever=vectordb.as_retriever(), | |
| # memory=memory | |
| # ) | |
| # return "✅ PDF processed! You can now chat.", gr.update(interactive=True), gr.update(interactive=True) | |
| # # Step 2: Chatbot logic | |
| # def chat_fn(message, history=[]): | |
| # if qa_chain is None: | |
| # return history + [[message, "⚠️ Please upload a PDF first."]], history | |
| # answer = qa_chain.run(message) | |
| # history.append((message, answer)) | |
| # return history, history | |
| # # Gradio UI | |
| # with gr.Blocks() as demo: | |
| # gr.Markdown("## 📄 Chat with PDF using Mistral 7B + LangChain") | |
| # upload = gr.File(label="Upload a PDF", file_types=[".pdf"]) | |
| # status = gr.Textbox(label="Status", interactive=False) | |
| # chatbot = gr.Chatbot(label="Chat") | |
| # message = gr.Textbox(label="Ask a question", placeholder="Type your question...", interactive=False) | |
| # send_btn = gr.Button("Send", interactive=False) | |
| # upload.change(fn=process_pdf, inputs=upload, outputs=[status, message, send_btn]) | |
| # send_btn.click(fn=chat_fn, inputs=message, outputs=[chatbot, chatbot]) | |
| # demo.launch(share=True) | |
| import os | |
| import gradio as gr | |
| from langchain_community.vectorstores import FAISS | |
| from langchain_community.embeddings import HuggingFaceEmbeddings | |
| from langchain_community.document_loaders import PyPDFLoader | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain.chains import ConversationalRetrievalChain | |
| from langchain.memory import ConversationBufferMemory | |
| from langchain_huggingface import HuggingFaceEndpoint | |
| # Set token for Hugging Face Inference API | |
| os.environ["HUGGINGFACEHUB_API_TOKEN"] = os.getenv("HUGGINGFACEHUB_API_TOKEN") | |
| # Load embedding model | |
| embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2") | |
| # Initialize LLM | |
| llm = HuggingFaceEndpoint( | |
| repo_id="mistralai/Mistral-7B-Instruct-v0.3", | |
| task="text-generation", | |
| max_new_tokens=128, | |
| temperature=0.5, | |
| ) | |
| # Step 1: Upload + Process PDF | |
| def process_pdf(pdf_file): | |
| loader = PyPDFLoader(pdf_file.name) | |
| pages = loader.load() | |
| splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50) | |
| chunks = splitter.split_documents(pages) | |
| vectordb = FAISS.from_documents(chunks, embeddings) | |
| memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) | |
| qa_chain = ConversationalRetrievalChain.from_llm( | |
| llm=llm, | |
| retriever=vectordb.as_retriever(), | |
| memory=memory | |
| ) | |
| return qa_chain, "✅ PDF processed! You can now chat.", gr.update(interactive=True), gr.update(interactive=True) | |
| # Step 2: Chatbot logic | |
| def chat_fn(message, chat_history, qa_chain): | |
| if qa_chain is None: | |
| return chat_history + [[message, "⚠️ Please upload a PDF first."]], chat_history, qa_chain | |
| answer = qa_chain.run(message) | |
| chat_history.append((message, answer)) | |
| return chat_history, chat_history, qa_chain | |
| # Gradio UI | |
| with gr.Blocks() as demo: | |
| gr.Markdown("## 📄 Chat with any PDF using Mistral 7B + LangChain") | |
| qa_chain_state = gr.State(None) | |
| chat_state = gr.State([]) | |
| upload = gr.File(label="Upload a PDF", file_types=[".pdf"]) | |
| status = gr.Textbox(label="Status", interactive=False) | |
| chatbot = gr.Chatbot(label="Chat") | |
| message = gr.Textbox(label="Ask a question", placeholder="Type your question...", interactive=False) | |
| send_btn = gr.Button("Send", interactive=False) | |
| upload.change(fn=process_pdf, inputs=upload, outputs=[qa_chain_state, status, message, send_btn]) | |
| send_btn.click(fn=chat_fn, inputs=[message, chat_state, qa_chain_state], outputs=[chatbot, chat_state, qa_chain_state]) | |
| demo.launch(share=True) | |