app.py

import gradio as gr
import os
from huggingface_hub import InferenceClient

# ---------------- CONFIG ----------------
HF_TOKEN = os.environ.get("HF_TOKEN")

client = InferenceClient(
    provider="hf-inference",
    api_key=HF_TOKEN,
)

print("HF TOKEN:", HF_TOKEN)


# ---------------- MOOD DETECTION ----------------
def detect_mood(text):
    text = text.lower()

    if any(w in text for w in ["sad", "down", "upset"]):
        return "sad"
    elif any(w in text for w in ["bored", "nothing to do"]):
        return "bored"
    elif any(w in text for w in ["stress", "tired", "overwhelmed"]):
        return "stressed"
    elif any(w in text for w in ["happy", "excited", "great"]):
        return "happy"
    else:
        return "neutral"


# ---------------- SUGGESTION ----------------
def suggest_activity(mood):
    suggestions = {
        "bored": "Try a quick 5-minute coding challenge.",
        "sad": "Go for a short walk or listen to music.",
        "stressed": "Take 5 deep breaths and focus on one small task.",
        "happy": "Channel this energy into something exciting!",
        "neutral": "Try organizing a small task or learning something new."
    }
    return suggestions.get(mood, suggestions["neutral"])


# ---------------- PERSONALITY ----------------
def apply_personality(text, personality):
    if personality == "Motivational Coach":
        return f"💪 You got this! {text}"
    elif personality == "Sarcastic Friend":
        return f"😏 Oh wow, emotions again. Anyway—{text}"
    elif personality == "Zen Monk":
        return f"🧘 Peace comes from within. {text}"
    return text


# ---------------- MODEL CALL ----------------
def query_model(prompt):
    try:
        response = client.chat.completions.create(
            model="bartowski/Qwen2.5-7B-Instruct-GGUF",
            messages=[
                {"role": "user", "content": prompt}
            ],
            max_tokens=60,
            temperature=0.7,
        )

        return response.choices[0].message["content"]

    except Exception as e:
        print("MODEL ERROR:", str(e))
        return "⚠️ Model is temporarily unavailable. Please try again."


# ---------------- CHAT FUNCTION ----------------
def respond(message, history, personality):
    mood = detect_mood(message)
    activity = suggest_activity(mood)

    prompt = f"""
You are a friendly and helpful assistant.

User mood: {mood}
User message: {message}

Respond naturally like a human in 1-2 sentences.
"""

    model_reply = query_model(prompt)

    model_reply = apply_personality(model_reply, personality)

    final_reply = f"{model_reply}\n\n👉 Suggestion: {activity}"

    history = history or []

    history.append({"role": "user", "content": message})
    history.append({"role": "assistant", "content": final_reply})

    return history


# ---------------- UI ----------------
with gr.Blocks() as demo:
    gr.Markdown("# 🧠 Mood-Based Life Assistant")

    personality = gr.Dropdown(
        ["Motivational Coach", "Sarcastic Friend", "Zen Monk"],
        value="Motivational Coach",
        label="Choose Personality"
    )

    chatbot = gr.Chatbot(type="messages")
    msg = gr.Textbox(placeholder="How are you feeling?")

    msg.submit(respond, [msg, chatbot, personality], [chatbot])

demo.launch()