Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+import random
+import logging
+import os
+import joblib
+
+# === Setup Logging ===
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(levelname)s - %(message)s",
+    handlers=[
+        logging.FileHandler("text_to_image.log"),
+        logging.StreamHandler()
+    ]
+)
+
+# === Load Model ===
+def load_model(model_path="model.pkl"):
+    try:
+        if not os.path.exists(model_path):
+            raise FileNotFoundError(f"Model not found at: {model_path}")
+        model = joblib.load(model_path)
+        logging.info("✅ Model loaded successfully.")
+        return model
+    except Exception as e:
+        logging.error(f"❌ Failed to load model: {e}")
+        return None
+
+# Dummy placeholder function (replace with actual inference)
+def dummy_text_to_image(prompt):
+    logging.info(f"🔍 Received prompt: {prompt}")
+    img = Image.new("RGB", (256, 256), (
+        random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
+    )
+    logging.info("🖼️ Returning dummy image.")
+    return img
+
+model = load_model()
+
+# === Gradio Interface ===
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("## 🖼️ Text to Image Generator - Minimal (Light Mode)")
+    prompt = gr.Textbox(placeholder="Enter your prompt here", label="Prompt")
+    output = gr.Image(label="Generated Image", type="pil")
+    generate = gr.Button("Generate")
+
+    generate.click(fn=dummy_text_to_image, inputs=prompt, outputs=output)
+
+demo.launch()