app.py

import os
import gradio as gr
import torch
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from transformers import AutoModelForCausalLM, AutoTokenizer

# Configuration
DOCS_DIR = ".business_docs"
EMBEDDING_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
MODEL_NAME = "microsoft/phi-3-mini-4k-instruct"  # CPU-optimized model

# System Initialization
def initialize_system():
    # Validate documents folder
    if not os.path.exists(DOCS_DIR):
        raise FileNotFoundError(f"Missing documents folder: {DOCS_DIR}")
    
    # Process PDFs
    pdf_files = [os.path.join(DOCS_DIR, f) for f in os.listdir(DOCS_DIR) if f.endswith(".pdf")]
    if not pdf_files:
        raise ValueError(f"No PDFs found in {DOCS_DIR}")

    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=512,  # Optimized for CPU
        chunk_overlap=50
    )
    
    documents = []
    for pdf_path in pdf_files:
        try:
            loader = PyPDFLoader(pdf_path)
            documents.extend(loader.load_and_split(text_splitter))
        except Exception as e:
            print(f"Error processing {pdf_path}: {str(e)}")
    
    # Create embeddings
    embeddings = HuggingFaceEmbeddings(
        model_name=EMBEDDING_MODEL,
        model_kwargs={'device': 'cpu'},
        encode_kwargs={'normalize_embeddings': True}
    )
    
    vector_store = FAISS.from_documents(documents, embeddings)
    
    # Load CPU-optimized model
    try:
        tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
        model = AutoModelForCausalLM.from_pretrained(
            MODEL_NAME,
            trust_remote_code=True,
            torch_dtype=torch.float32,
            device_map="cpu"
        )
    except Exception as e:
        raise RuntimeError(f"Model loading failed: {str(e)}")
    
    return vector_store, model, tokenizer

# Initialize system
try:
    vector_store, model, tokenizer = initialize_system()
    print("✅ System ready with business documents")
except Exception as e:
    print(f"❌ Initialization failed: {str(e)}")
    raise

# Response Generation
def generate_response(query):
    try:
        # Context retrieval
        docs = vector_store.similarity_search(query, k=2)
        context = "\n".join([d.page_content for d in docs])
        
        # Phi-3 prompt template
        prompt = f"""<|system|>
        Answer ONLY using the business documents. Respond to unknown queries with: "This information is not available in our current documentation."
        
        Context: {context}</s>
        <|user|>
        {query}</s>
        <|assistant|>
        """
        
        # Generate response
        inputs = tokenizer(prompt, return_tensors="pt", return_attention_mask=False)
        outputs = model.generate(
            inputs.input_ids,
            max_new_tokens=200,
            temperature=0.1,
            do_sample=True,
            pad_token_id=tokenizer.eos_token_id
        )
        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
        return response.split("<|assistant|>")[-1].strip()
    
    except Exception as e:
        return f"Error: Please try again. ({str(e)[:50]})"

# Gradio Interface
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 📚 Business Documentation Assistant")
    
    chatbot = gr.Chatbot(height=300)
    msg = gr.Textbox(placeholder="Ask about our services...", label="")
    clear = gr.Button("Clear History")
    
    def respond(message, history):
        response = generate_response(message)
        history.append((message, response))
        return "", history
    
    msg.submit(respond, [msg, chatbot], [msg, chatbot])
    clear.click(lambda: None, None, chatbot, queue=False)

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)