app.py

import gradio as gr, json, plotly.graph_objects as go
from transformers import pipeline
from PIL import Image, ImageDraw

# ----------------------------
# Load instruction-tuned text model (fast on CPU)
# ----------------------------
chat_model = pipeline("text2text-generation", model="google/flan-t5-small", device=-1)

def query_llm(prompt, history, persona):
    # History is ignored here because flan-t5 is single-turn
    if persona != "Default":
        prompt = f"As a {persona}, {prompt}"
    out = chat_model(prompt, max_new_tokens=150)
    return out[0]["generated_text"].strip()

def make_placeholder_image(prompt: str):
    img = Image.new("RGB", (512, 512), color=(30, 30, 60))
    d = ImageDraw.Draw(img)
    d.text((20, 20), f"[Sketch of: {prompt}]", fill=(200, 200, 255))
    return img

def multimodal_chat(user_msg, history, persona):
    history = history or []
    assistant_content = query_llm(user_msg, history, persona)

    # Very simple routing: look for keywords
    img, fig = None, None
    if "chart" in user_msg.lower():
        fig = go.Figure()
        fig.add_trace(go.Scatter(x=[2010, 2020, 2030], y=[5, 50, 200], mode="lines+markers", name="AI Adoption"))
        fig.update_layout(title="AI Adoption Over Time")
        history.append([user_msg, "📊 Here's a chart of AI adoption"])
    elif "simulate" in user_msg.lower():
        steps = ["Aliens send a signal", "Humans decode it", "First meeting arranged"]
        history.append([user_msg, "🔮 Simulation: First Contact\n" + "\n".join([f"→ {s}" for s in steps])])
    elif "draw" in user_msg.lower() or "image" in user_msg.lower():
        img = make_placeholder_image(user_msg)
        history.append([user_msg, f"🖼️ (Placeholder image for: {user_msg})"])
    else:
        history.append([user_msg, assistant_content])

    return history, img, fig

# ----------------------------
# Gradio UI
# ----------------------------
with gr.Blocks(css="style.css") as demo:
    gr.Markdown("🧠 **ZEN Research Lab (CPU-Safe Edition)**", elem_id="zen-header")

    gr.Markdown("✅ Text  ✅ Charts  ✅ Simulation  ✅ Placeholder Images (no GPU needed)")

    persona = gr.Dropdown(["Default","Analyst","Artist","Futurist","Philosopher"], label="Mode", value="Default")
    chatbot = gr.Chatbot(label="Conversation", height=400)

    with gr.Row():
        user_msg = gr.Textbox(placeholder="Ask me anything…", label="Your message", scale=4)
        send_btn = gr.Button("Send", variant="primary")

    img_out   = gr.Image(label="Generated image")
    chart_out = gr.Plot(label="Interactive chart")

    def respond(user_msg, chat_history, persona):
        chat_history, img, fig = multimodal_chat(user_msg, chat_history, persona)
        return (
            chat_history,
            gr.update(value=img) if img else gr.update(value=None),
            gr.update(value=fig) if fig else gr.update(value=None)
        )

    send_btn.click(respond, inputs=[user_msg, chatbot, persona],
                   outputs=[chatbot, img_out, chart_out])
    user_msg.submit(respond, inputs=[user_msg, chatbot, persona],
                    outputs=[chatbot, img_out, chart_out])

    # Examples
    with gr.Accordion("✨ Try these examples"):
        gr.Examples(
            examples=[
                ["Draw a futuristic city skyline at night"],
                ["Simulate first contact with an alien civilization"],
                ["Make a chart of AI adoption from 2010 to 2030"],
                ["Explain quantum entanglement in simple terms"],
            ],
            inputs=[user_msg]
        )

if __name__ == "__main__":
    demo.queue(max_size=50).launch()