Create app.py

app.py

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+import gradio as gr, json, plotly.graph_objects as go
+from transformers import pipeline
+from diffusers import StableDiffusionPipeline
+import torch
+
+# ----------------------------
+# Load models once on startup
+# ----------------------------
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Text model (fast chat)
+chat_model = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-beta", device=0 if device=="cuda" else -1)
+
+# Image model (stable diffusion)
+sd_model = StableDiffusionPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16 if device=="cuda" else torch.float32
+).to(device)
+
+
+SYSTEM_PROMPT = """You are ZEN Research Assistant.
+You can respond in ONE of these forms:
+- Image → {"type":"image","prompt":"<prompt>"}
+- Chart → {"type":"chart","title":"<chart title>","data":[{"x":[...], "y":[...], "label":"<series>"}]}
+- Simulation → {"type":"simulation","topic":"<title>","steps":["...", "..."]}
+- Text → plain conversation, explanation, or reasoning.
+
+Rules:
+- Use JSON ONLY for image, chart, or simulation.
+- Simulation = imaginative thought experiment, 3–6 steps.
+- If not sure, default to conversational text.
+"""
+
+def query_llm(prompt, history, persona):
+    # Construct conversation
+    input_text = SYSTEM_PROMPT
+    if persona != "Default":
+        input_text += f"\nPersona: {persona}\n"
+    for u, a in history:
+        input_text += f"User: {u}\nAssistant: {a}\n"
+    input_text += f"User: {prompt}\nAssistant:"
+
+    out = chat_model(input_text, max_new_tokens=400, do_sample=True, temperature=0.7)
+    return out[0]["generated_text"].split("Assistant:")[-1].strip()
+
+
+def multimodal_chat(user_msg, history, persona):
+    history = history or []
+    assistant_content = query_llm(user_msg, history, persona)
+
+    img, fig = None, None
+    try:
+        parsed = json.loads(assistant_content)
+
+        if parsed.get("type") == "image":
+            img = sd_model(parsed["prompt"]).images[0]
+            history.append([user_msg, "🖼️ Generated image below."])
+
+        elif parsed.get("type") == "chart":
+            fig = go.Figure()
+            for s in parsed["data"]:
+                fig.add_trace(go.Scatter(
+                    x=s["x"], y=s["y"], mode="lines+markers", name=s.get("label","")
+                ))
+            fig.update_layout(title=parsed.get("title","Chart"))
+            history.append([user_msg, parsed.get("title","Chart below")])
+
+        elif parsed.get("type") == "simulation":
+            steps = "\n".join([f"→ {s}" for s in parsed["steps"]])
+            history.append([user_msg, f"🔮 Simulation: {parsed.get('topic','Exploration')}\n{steps}"])
+
+        else:
+            history.append([user_msg, assistant_content])
+
+    except (json.JSONDecodeError, KeyError, TypeError):
+        history.append([user_msg, assistant_content])
+
+    return history, img, fig
+
+
+with gr.Blocks(css="style.css") as demo:
+    gr.Markdown("🧠 **ZEN Research Lab (API-free Edition)** — Explore, simulate, and create", elem_id="zen-header")
+
+    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])
+
+if __name__ == "__main__":
+    demo.queue(max_size=50).launch()