Update app.py

app.py

@@ -1,42 +1,23 @@
-import gradio as gr, json, plotly.graph_objects as go, torch
+import gradio as gr, json, plotly.graph_objects as go
 from transformers import pipeline
+from PIL import Image, ImageDraw, ImageFont
 
 # ----------------------------
-# Detect device
+# Load a tiny text model (works on cpu-basic)
 # ----------------------------
-device = "cuda" if torch.cuda.is_available() else "cpu"
+chat_model = pipeline("text-generation", model="distilgpt2", device=-1)
 
-# ----------------------------
-# Load lightweight text model
-# ----------------------------
-text_model_name = "google/flan-t5-small"  # tiny, CPU-friendly
-chat_model = pipeline("text2text-generation", model=text_model_name, device=0 if device=="cuda" else -1)
-
-# ----------------------------
-# Try to load Stable Diffusion (only if GPU)
-# ----------------------------
-sd_model = None
-if device == "cuda":
-    try:
-        from diffusers import StableDiffusionPipeline
-        sd_model = StableDiffusionPipeline.from_pretrained(
-            "stabilityai/stable-diffusion-2-1-base"
-        ).to(device)
-    except Exception as e:
-        print("⚠️ Could not load Stable Diffusion:", e)
-        sd_model = None
-
-# ----------------------------
-# Core assistant logic
-# ----------------------------
 SYSTEM_PROMPT = """You are ZEN Research Assistant.
-You can respond in ONE of these forms:
+Reply in ONE of these forms:
 - Image → {"type":"image","prompt":"<prompt>"}
 - Chart → {"type":"chart","title":"<chart title>","data":[{"x":[1,2,3], "y":[2,4,6], "label":"Example"}]}
 - Simulation → {"type":"simulation","topic":"<title>","steps":["...", "..."]}
-- Text → plain conversation.
+- Text → plain explanation or reasoning.
 """
 
+# ----------------------------
+# Helpers
+# ----------------------------
 def query_llm(prompt, history, persona):
     input_text = SYSTEM_PROMPT
     if persona != "Default":
@@ -45,9 +26,20 @@ def query_llm(prompt, history, persona):
         input_text += f"User: {u}\nAssistant: {a}\n"
     input_text += f"User: {prompt}\nAssistant:"
 
-    out = chat_model(input_text, max_new_tokens=256)
+    out = chat_model(input_text, max_new_tokens=150, do_sample=True, temperature=0.7)
     return out[0]["generated_text"].strip()
 
+def make_placeholder_image(prompt: str):
+    """Creates a simple placeholder image with the prompt written on it"""
+    img = Image.new("RGB", (512, 512), color=(30, 30, 60))
+    d = ImageDraw.Draw(img)
+    try:
+        font = ImageFont.truetype("DejaVuSans.ttf", 22)
+    except:
+        font = None
+    d.text((20, 20), f"[Sketch of: {prompt}]", fill=(200, 200, 255), font=font)
+    return img
+
 def multimodal_chat(user_msg, history, persona):
     history = history or []
     assistant_content = query_llm(user_msg, history, persona)
@@ -57,11 +49,8 @@ def multimodal_chat(user_msg, history, persona):
         parsed = json.loads(assistant_content)
 
         if parsed.get("type") == "image":
-            if sd_model is not None:
-                img = sd_model(parsed["prompt"]).images[0]
-                history.append([user_msg, "🖼️ Generated image below."])
-            else:
-                history.append([user_msg, "⚠️ Image generation requires GPU."])
+            img = make_placeholder_image(parsed["prompt"])
+            history.append([user_msg, f"🖼️ (Placeholder image for: {parsed['prompt']})"])
 
         elif parsed.get("type") == "chart":
             fig = go.Figure()
@@ -79,7 +68,8 @@ def multimodal_chat(user_msg, history, persona):
         else:
             history.append([user_msg, assistant_content])
 
-    except (json.JSONDecodeError, KeyError, TypeError):
+    except Exception:
+        # fallback: plain text
         history.append([user_msg, assistant_content])
 
     return history, img, fig
@@ -88,14 +78,9 @@ def multimodal_chat(user_msg, history, persona):
 # Gradio UI
 # ----------------------------
 with gr.Blocks(css="style.css") as demo:
-    gr.Markdown("🧠 **ZEN Research Lab (Light Mode)**", elem_id="zen-header")
-
-    cap_text = "✅ Text  ✅ Charts  ✅ Simulation"
-    if sd_model is not None:
-        cap_text += "  ✅ Images"
-    else:
-        cap_text += "  ❌ Images (GPU required)"
-    gr.Markdown(cap_text)
+    gr.Markdown("🧠 **ZEN Research Lab (Guaranteed-to-Run 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)