app.py

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import List, Dict, Any
from pymongo import MongoClient
from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
import spacy
import os
import logging

# Set up logging with detailed output
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

app = FastAPI()

# MongoDB Setup
connection_string = os.getenv("MONGO_URI", "mongodb+srv://clician:[email protected]/?retryWrites=true&w=majority&appName=Hutterdev")
client = MongoClient(connection_string)
db = client["test"]
products_collection = db["products"]

# BlenderBot Setup
model_repo = "SyedHutter/blenderbot_model"
model_subfolder = "blenderbot_model"
model_dir = "/home/user/app/blenderbot_model"

if not os.path.exists(model_dir):
    logger.info(f"Downloading {model_repo}/{model_subfolder} to {model_dir}...")
    tokenizer = BlenderbotTokenizer.from_pretrained(model_repo, subfolder=model_subfolder)
    model = BlenderbotForConditionalGeneration.from_pretrained(model_repo, subfolder=model_subfolder)
    os.makedirs(model_dir, exist_ok=True)
    tokenizer.save_pretrained(model_dir)
    model.save_pretrained(model_dir)
    logger.info("Model download complete.")
else:
    logger.info(f"Loading pre-existing model from {model_dir}.")

tokenizer = BlenderbotTokenizer.from_pretrained(model_dir)
model = BlenderbotForConditionalGeneration.from_pretrained(model_dir)

# Static Context
context_msg = "Hutter Products GmbH provides sustainable products like shirts and shorts..."

# spaCy Setup
spacy_model_path = "/home/user/app/en_core_web_sm-3.8.0"
nlp = spacy.load(spacy_model_path)

# Pydantic Models
class PromptRequest(BaseModel):
    input_text: str
    conversation_history: List[str] = []

class CombinedResponse(BaseModel):
    ner: Dict[str, Any]
    qa: Dict[str, Any]
    products_matched: List[Dict[str, Any]]

# Helper Functions
def extract_keywords(text: str) -> List[str]:
    doc = nlp(text)  # Fixed syntax error here
    keywords = [token.text for token in doc if token.pos_ in ["NOUN", "PROPN"]]
    return list(set(keywords))

def detect_intent(text: str) -> str:
    doc = nlp(text.lower())
    if any(token.text in ["shirt", "shirts"] for token in doc):
        return "recommend_shirt"
    elif any(token.text in ["short", "shorts"] for token in doc):
        return "recommend_shorts"
    elif any(token.text in ["what", "who", "company", "do", "products"] for token in doc):
        return "company_info"
    return "unknown"

def search_products_by_keywords(keywords: List[str]) -> List[Dict[str, Any]]:
    if not keywords:
        logger.info("No keywords provided, returning empty product list.")
        return []
    query = {"$or": [{"name": {"$regex": keyword, "$options": "i"}} for keyword in keywords]}
    matched_products = [dict(p, id=str(p["_id"])) for p in products_collection.find(query)]
    return matched_products

def get_product_context(products: List[Dict]) -> str:
    if not products:
        return ""
    product_str = "Here are some products: "
    product_str += ", ".join([f"'{p['name']}' (SKU: {p['skuNumber']}) - {p['description']}" for p in products[:2]])
    return product_str

def format_response(response: str, products: List[Dict], intent: str) -> str:
    if intent in ["recommend_shirt", "recommend_shorts"] and products:
        product = products[0]
        return f"{response} For example, check out our '{product['name']}' (SKU: {product['skuNumber']})—it’s {product['description'].lower()}!"
    elif intent == "company_info":
        return f"{response} At Hutter Products GmbH, we specialize in sustainable product design and production!"
    return response

# Endpoints
@app.get("/")
async def root():
    return {"message": "Welcome to the NER and Chat API!"}

@app.post("/process/", response_model=CombinedResponse)
async def process_prompt(request: PromptRequest):
    try:
        logger.info(f"Processing request: {request.input_text}")
        input_text = request.input_text
        history = request.conversation_history[-3:] if request.conversation_history else []

        intent = detect_intent(input_text)
        keywords = extract_keywords(input_text)
        logger.info(f"Intent: {intent}, Keywords: {keywords}")

        products = search_products_by_keywords(keywords)
        product_context = get_product_context(products)
        logger.info(f"Products matched: {len(products)}")

        history_str = " || ".join(history)
        full_input = f"{history_str} || {product_context} {context_msg} || {input_text}" if history else f"{product_context} {context_msg} || {input_text}"
        logger.info(f"Full input to model: {full_input}")

        logger.info("Tokenizing input...")
        inputs = tokenizer(full_input, return_tensors="pt", truncation=True, max_length=512)
        logger.info("Input tokenized successfully.")

        logger.info("Generating model response...")
        outputs = model.generate(**inputs, max_length=50, num_beams=1, no_repeat_ngram_size=2)
        logger.info("Model generation complete.")

        logger.info("Decoding model output...")
        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
        logger.info(f"Model response: {response}")

        enhanced_response = format_response(response, products, intent)
        qa_response = {
            "question": input_text,
            "answer": enhanced_response,
            "score": 1.0
        }

        logger.info("Returning response...")
        return {
            "ner": {"extracted_keywords": keywords},
            "qa": qa_response,
            "products_matched": products
        }
    except Exception as e:
        logger.error(f"Error processing request: {str(e)}", exc_info=True)
        raise HTTPException(status_code=500, detail=f"Oops, something went wrong: {str(e)}. Try again!")

@app.on_event("startup")
async def startup_event():
    logger.info("API is running with BlenderBot-400M-distill, connected to MongoDB.")

@app.on_event("shutdown")
def shutdown_event():
    client.close()