app.py

import json
import difflib
import gradio as gr
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# -------------------------------------------------
# Load Knowledge Base
# -------------------------------------------------
with open("destinations.json", "r", encoding="utf-8") as f:
    DESTS = json.load(f)

DEST_NAMES = [d["name"] for d in DESTS]

def retrieve_destinations(query, n=3):
    query = query.lower()
    exact = [
        d for d in DESTS
        if d["name"].lower() in query or any(tag.lower() in query for tag in d.get("tags", []))
    ]

    if exact:
        return exact[:n]

    matches = difflib.get_close_matches(query, DEST_NAMES, n=n, cutoff=0.4)
    return [d for d in DESTS if d["name"] in matches]


def build_prompt(user_message: str, retrieved):
    kb_text = ""
    if retrieved:
        details = []
        for d in retrieved:
            details.append(
                f"{d['name']} — {d['summary']}\n"
                f"Top attractions: {', '.join(d['top_attractions'])}\n"
                f"Best months: {d['best_months']}"
            )
        kb_text = "Destination Knowledge:\n" + "\n\n".join(details) + "\n\n"

    return (
        f"{kb_text}"
        f"User Query: \"{user_message}\"\n"
        "You are a helpful travel guide. Provide:\n"
        "- Best suited destination(s)\n"
        "- Why it matches the user's need\n"
        "- Best time to visit\n"
        "- 2–3 activities to do\n"
        "- Short travel/safety tips"
    )


# -------------------------------------------------
# Load CPU-friendly Model
# -------------------------------------------------
MODEL = "facebook/blenderbot-400M-distill"

tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL)   # CPU ONLY → no .to('cuda')


def chatbot_reply(message, history):
    retrieved = retrieve_destinations(message)
    prompt = build_prompt(message, retrieved)

    inputs = tokenizer(prompt, return_tensors="pt", truncation=True)
    outputs = model.generate(
        **inputs,
        max_new_tokens=200,
        do_sample=True,
        top_p=0.9,
        temperature=0.6
    )
    reply = tokenizer.decode(outputs[0], skip_special_tokens=True)

    history.append((message, reply))
    return history, history


# -------------------------------------------------
# Gradio UI
# -------------------------------------------------
with gr.Blocks() as demo:
    gr.Markdown("# 🌍 Travel Recommendation Chatbot (CPU Only)")

    chatbox = gr.Chatbot()
    txt = gr.Textbox(placeholder="Ask: 'Where should I travel in winter for beaches?'", label="Your Message")
    state = gr.State([])

    txt.submit(chatbot_reply, [txt, state], [chatbox, state])

demo.launch()