app.py

import gradio as gr
from transformers import pipeline

# Load sentiment analysis model locally
sentiment_pipeline = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")

# Simple chatbot logic
def chatbot(user_input, history=[]):
    # Analyse sentiment
    sentiment = sentiment_pipeline(user_input)[0]
    label = sentiment['label']
    score = sentiment['score']

    # Generate empathetic response
    if label == "POSITIVE":
        bot_reply = f"😊 I'm glad to hear that! You sound positive. (Confidence: {score:.2f})"
    elif label == "NEGATIVE":
        bot_reply = f"💙 I’m sorry you’re feeling this way. Remember, tough times pass. (Confidence: {score:.2f})"
    else:
        bot_reply = f"🤔 I see. Thanks for sharing with me. (Confidence: {score:.2f})"

    # Maintain chat history
    history.append((user_input, bot_reply))
    return history, history

# Build UI
with gr.Blocks() as demo:
    gr.Markdown("## 🧠 MindM8 – Offline Sentiment Chatbot")
    chatbot_ui = gr.Chatbot()
    msg = gr.Textbox(label="Type your message...")
    state = gr.State([])

    msg.submit(chatbot, [msg, state], [chatbot_ui, state])
    msg.submit(lambda _: "", None, msg)  # Clear input after send

# Launch
demo.launch()