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| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| from langchain.chains import RetrievalQA | |
| from langchain.embeddings import HuggingFaceEmbeddings | |
| from langchain.vectorstores import FAISS | |
| from langchain.llms import HuggingFaceHub | |
| from langchain.chains import ConversationalRetrievalChain | |
| from unstructured.documents import from_pdf | |
| import camelot | |
| from pathlib import Path | |
| # Load the HuggingFace language model and embeddings | |
| client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
| embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") | |
| vector_store = None | |
| retriever = None | |
| def extract_text_from_pdf(filepath): | |
| # Use unstructured to read text from the PDF | |
| documents = from_pdf(filepath) | |
| return "\n".join([doc.text for doc in documents]) | |
| def extract_tables_from_pdf(filepath): | |
| # Use camelot to read tables from the PDF | |
| tables = camelot.read_pdf(filepath, pages='1-end') | |
| return [table.df.to_string(index=False) for table in tables] | |
| def update_documents(text_input): | |
| global vector_store, retriever | |
| documents = text_input.split("\n") | |
| vector_store = FAISS.from_texts(documents, embeddings) | |
| retriever = vector_store.as_retriever() | |
| return f"{len(documents)} documents successfully added to the vector store." | |
| rag_chain = None | |
| def respond(message, history, system_message, max_tokens, temperature, top_p): | |
| global rag_chain, retriever | |
| if retriever is None: | |
| return "Please upload or enter documents before asking a question." | |
| if rag_chain is None: | |
| rag_chain = ConversationalRetrievalChain.from_llm( | |
| HuggingFaceHub(repo_id="HuggingFaceH4/zephyr-7b-beta"), | |
| retriever=retriever | |
| ) | |
| conversation_history = [{"role": "system", "content": system_message}] | |
| for val in history: | |
| if val[0]: | |
| conversation_history.append({"role": "user", "content": val[0]}) | |
| if val[1]: | |
| conversation_history.append({"role": "assistant", "content": val[1]}) | |
| conversation_history.append({"role": "user", "content": message}) | |
| response = rag_chain({"question": message, "chat_history": history}) | |
| return response['answer'] | |
| def upload_file(filepath): | |
| text = extract_text_from_pdf(filepath) | |
| tables = extract_tables_from_pdf(filepath) | |
| # Update documents in the vector store | |
| update_documents(text) | |
| return [gr.UploadButton(visible=False), gr.DownloadButton(label=f"Download {Path(filepath).name}", value=filepath, visible=True), f"{len(tables)} tables extracted."] | |
| # Gradio interface setup | |
| demo = gr.Blocks() | |
| with demo: | |
| with gr.Row(): | |
| u = gr.UploadButton("Upload a file", file_count="single") | |
| d = gr.DownloadButton("Download the file", visible=False) | |
| u.upload(upload_file, u, [u, d, "status"]) | |
| with gr.Row(): | |
| chat = gr.ChatInterface( | |
| respond, | |
| additional_inputs=[ | |
| gr.Textbox(value="You are a helpful assistant.", label="System message"), | |
| gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), | |
| gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), | |
| gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"), | |
| ], | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |