app.py

import os
import gradio as gr
from huggingface_hub import hf_hub_download
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_groq import ChatGroq
from langchain_community.document_loaders import PyPDFLoader
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain

# Configuration
HF_REPO_ID = "Shami96/7solar-documentation"
HF_PDF_NAME = "7solar_documentation.pdf"
HF_TOKEN = os.environ.get("HF_TOKEN")

# Initialize components
def initialize_components():
    print("⚙️ Initializing components...")
    
    # Load PDF
    try:
        pdf_path = hf_hub_download(
            repo_id=HF_REPO_ID,
            filename=HF_PDF_NAME,
            repo_type="dataset",
            token=HF_TOKEN
        )
        loader = PyPDFLoader(pdf_path)
        documents = loader.load()
    except Exception as e:
        raise RuntimeError(f"Failed to load PDF: {str(e)}")

    # Create vector store
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=1000,
        chunk_overlap=200
    )
    chunks = text_splitter.split_documents(documents)
    embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
    vectorstore = Chroma.from_documents(chunks, embeddings)
    
    # Initialize LLM
    llm = ChatGroq(
        model_name="llama3-70b-8192",
        temperature=0.3
    )
    
    # Create conversation memory
    memory = ConversationBufferMemory(
        memory_key="chat_history",
        return_messages=True
    )
    
    # Create retrieval chain with memory
    qa_chain = ConversationalRetrievalChain.from_llm(
        llm=llm,
        retriever=vectorstore.as_retriever(),
        memory=memory,
        chain_type="stuff"
    )
    
    return qa_chain

# Chat function
def respond(message, history):
    try:
        # Initialize if not already done
        if 'qa_chain' not in globals():
            global qa_chain
            qa_chain = initialize_components()
        
        # Handle greetings
        if message.lower() in ["hi", "hello", "hey"]:
            return "Hello! I'm your 7Solar assistant. How can I help you today?"
        
        # Get response with conversation context
        result = qa_chain({"question": message})
        return result["answer"]
    
    except Exception as e:
        return f"An error occurred: {str(e)}"

# Create Gradio interface
demo = gr.ChatInterface(
    fn=respond,
    title="☀️ 7Solar Assistant",
    description="Ask me anything about 7Solar's services and documentation",
    examples=["What is 7Solar.pk?", "How does the registration process work?"],
    cache_examples=False
)

# Launch with error handling
if __name__ == "__main__":
    try:
        demo.launch(server_name="0.0.0.0", server_port=7860)
    except Exception as e:
        print(f"Failed to launch: {str(e)}")
        raise