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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)
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