app.py

import os
import gradio as gr
from groq import Groq
from datasets import load_dataset

GROQ_MODEL = "llama3-70b-8192"
DATASET_NAME = "embedding-data/Amazon-QA"

def load_shopify_context():
    dataset = load_dataset(DATASET_NAME)
    samples = dataset['train'].select(range(3))
    examples = []
    for sample in samples:
        question = sample['query']
        if isinstance(question, list):
            question = question[0] if len(question) > 0 else "No question"
        question = str(question).replace('\\', '/')
        answer = sample.get('pos', sample.get('answer', ["No answer"]))
        if isinstance(answer, list):
            answer = answer[0] if len(answer) > 0 else "No answer"
        answer = str(answer).replace('\\', '/')
        examples.append(f"Q: {question}\nA: {answer}")
    return '\n'.join(examples)

def generate_response(message, history):
    api_key = os.getenv("Mujtaba_shopify_chatbot_key")
    if not api_key:
        return "Error: GROQ_API_KEY not set. Please add it as a secret in your Space."
    client = Groq(api_key=api_key)
    context = load_shopify_context()
    conversation = []
    for user_msg, bot_msg in history:
        safe_user = str(user_msg).replace('\\', '/')
        safe_bot = str(bot_msg).replace('\\', '/')
        conversation.extend([f"User: {safe_user}", f"Assistant: {safe_bot}"])
    safe_message = str(message).replace('\\', '/')
    prompt = f"You are an expert Shopify support agent. Context examples:\n{context}\n{chr(10).join(conversation)}\nUser: {safe_message}\nAssistant:"
    try:
        response = client.chat.completions.create(
            messages=[{"role": "user", "content": prompt}],
            model=GROQ_MODEL,
            temperature=0.7,
            max_tokens=256,
            top_p=0.9,
            stop=["<|endoftext|>"]
        )
        return response.choices[0].message.content
    except Exception as e:
        return f"Error: {str(e)}"

with gr.Blocks() as app:
    gr.Markdown("## Shopify Q&A Assistant (Groq-powered)")
    gr.ChatInterface(
        fn=generate_response,
        examples=[
            "What's your return policy?",
            "Do you ship internationally?",
            "Is this compatible with iPhone 15?"
        ]
    )

app.launch()