Update app.py

app.py

@@ -241,11 +241,24 @@ def auto_enhance_image_api():
 
     return jsonify({"error": "An unknown error occurred"}), 500
 
-# --- AI IMAGE GENERATOR (TEXT-TO-IMAGE) API ENDPOINT ---
-
-# Model ab pehle se hi download ho chuka hai. Hum usko yahan load karenge.
-# Yeh ab crash nahi hoga kyunki files pehle se cache mein hain.
-pipe = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch")
+# --- AI IMAGE GENERATOR (TEXT-TO-IMAGE) API ENDPOINT (FINAL, GUARANTEED LAZY LOADING) ---
+
+# Hum model ke liye ek global "box" (variable) banayenge, lekin usko start mein khali rakhenge.
+pipe = None
+
+def get_pipeline():
+    """
+    Yeh ek helper function hai jo model ko sirf ek baar load karega.
+    """
+    global pipe
+    if pipe is None:
+        print("Model not loaded. Loading Tiny Stable Diffusion model for the first time...")
+        # Note: Secrets (like HUGGING_FACE_HUB_CACHE) will be available here.
+        pipe = DiffusionPipeline.from_pretrained(
+            "hf-internal-testing/tiny-stable-diffusion-torch"
+        )
+        print("Model loaded successfully into memory.")
+    return pipe
 
 @app.route('/generate-image', methods=['POST'])
 def generate_image_api():
@@ -264,7 +277,14 @@ def generate_image_api():
 
     # AI Model se Image Generate Karna
     try:
-        image = pipe(prompt, num_inference_steps=10).images[0]
+        # Har request per, hum helper function ko call karke model haasil karenge.
+        # Agar model pehle se loaded hai, to woh foran mil jayega.
+        # Agar nahi, to woh ab load hoga.
+        pipeline = get_pipeline()
+        
+        print(f"Generating image for prompt: {prompt}")
+        image = pipeline(prompt, num_inference_steps=10).images[0]
+        print("Image generated.")
         
         output_buffer = io.BytesIO()
         image.save(output_buffer, format='PNG')