Update app.py

app.py

@@ -1,95 +1,133 @@
 import os
-import torch
+import requests
 import gradio as gr
-from diffusers import DiffusionPipeline
 from PIL import Image, ImageDraw, ImageFont
+import io
+import time
+from concurrent.futures import ThreadPoolExecutor
 
-# ===== FREE-TIER CONFIG =====
+# ===== CONFIGURATION =====
+HF_API_TOKEN = os.environ.get("HF_API_TOKEN")
+MODEL_NAME = "stabilityai/stable-diffusion-xl-base-1.0"
+API_URL = f"https://api-inference.huggingface.co/models/{MODEL_NAME}"
+headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
 WATERMARK_TEXT = "SelamGPT"
-MODEL_NAME = "DeepFloyd/IF-II-L-v1.0"
+MAX_RETRIES = 3
+TIMEOUT = 60
+EXECUTOR = ThreadPoolExecutor(max_workers=2)
 
-# Initialize pipeline (lazy load later)
-pipe = None
-
-def load_model():
-    global pipe
-    if pipe is None:
-        pipe = DiffusionPipeline.from_pretrained(
-            MODEL_NAME,
-            torch_dtype=torch.float16,
-            variant="fp16"
-        )
-        pipe.to("cuda")
-
-# ===== OPTIMIZED WATERMARK =====
-def add_watermark(image):
+# ===== WATERMARK FUNCTION =====
+def add_watermark(image_bytes):
+    """Add watermark with optimized PNG output"""
     try:
+        image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
         draw = ImageDraw.Draw(image)
-        font = ImageFont.load_default(20)
+        
+        font_size = 24
+        try:
+            font = ImageFont.truetype("Roboto-Bold.ttf", font_size)
+        except:
+            font = ImageFont.load_default(font_size)
+        
         text_width = draw.textlength(WATERMARK_TEXT, font=font)
-        draw.text(
-            (image.width - text_width - 15, image.height - 30),
-            WATERMARK_TEXT,
-            font=font,
-            fill=(255, 255, 255)
-        )
-        return image
-    except Exception:
-        return image
+        x = image.width - text_width - 10
+        y = image.height - 34
+        
+        draw.text((x+1, y+1), WATERMARK_TEXT, font=font, fill=(0, 0, 0, 128))
+        draw.text((x, y), WATERMARK_TEXT, font=font, fill=(255, 255, 255))
+        
+        # Convert to optimized PNG
+        img_byte_arr = io.BytesIO()
+        image.save(img_byte_arr, format='PNG', optimize=True, quality=85)
+        img_byte_arr.seek(0)
+        return Image.open(img_byte_arr)
+    except Exception as e:
+        print(f"Watermark error: {str(e)}")
+        return Image.open(io.BytesIO(image_bytes))
 
-# ===== GENERATION FUNCTION =====
+# ===== IMAGE GENERATION =====
 def generate_image(prompt):
     if not prompt.strip():
         return None, "⚠️ Please enter a prompt"
     
-    try:
-        load_model()
-        
-        result = pipe(
-            prompt=prompt,
-            output_type="pil",
-            generator=torch.Generator(device="cuda").manual_seed(42),
-            num_inference_steps=30,
-            guidance_scale=7.0
+    def api_call():
+        return requests.post(
+            API_URL,
+            headers=headers,
+            json={
+                "inputs": prompt,
+                "parameters": {
+                    "height": 1024,
+                    "width": 1024,
+                    "num_inference_steps": 30
+                },
+                "options": {"wait_for_model": True}
+            },
+            timeout=TIMEOUT
         )
-        
-        return add_watermark(result.images[0]), "✔️ Generation successful"
-    except torch.cuda.OutOfMemoryError:
-        return None, "⚠️ Out of memory - Try a simpler prompt"
-    except Exception as e:
-        return None, f"⚠️ Error: {str(e)[:200]}"
+    
+    for attempt in range(MAX_RETRIES):
+        try:
+            future = EXECUTOR.submit(api_call)
+            response = future.result()
+            
+            if response.status_code == 200:
+                return add_watermark(response.content), "✔️ Generation successful"
+            elif response.status_code == 503:
+                wait_time = (attempt + 1) * 15
+                print(f"Model loading, waiting {wait_time}s...")
+                time.sleep(wait_time)
+                continue
+            else:
+                return None, f"⚠️ API Error: {response.text[:200]}"
+        except requests.Timeout:
+            return None, f"⚠️ Timeout: Model took >{TIMEOUT}s to respond"
+        except Exception as e:
+            return None, f"⚠️ Unexpected error: {str(e)[:200]}"
+    
+    return None, "⚠️ Failed after multiple attempts. Please try later."
+
+# ===== GRADIO THEME =====
+theme = gr.themes.Default(
+    primary_hue="emerald",
+    secondary_hue="amber",
+    font=[gr.themes.GoogleFont("Poppins"), "Arial", "sans-serif"]
+)
 
 # ===== GRADIO INTERFACE =====
-with gr.Blocks(title="SelamGPT Pro") as demo:
+with gr.Blocks(theme=theme, title="SelamGPT Image Generator") as demo:
     gr.Markdown("""
-    # 🎨 SelamGPT (DeepFloyd IF-II-L)
-    *Free Tier Optimized - May take 2-3 minutes for first generation*
+    # 🎨 SelamGPT Image Generator
+    *Powered by Stable Diffusion XL (1024x1024 PNG output)*
     """)
     
     with gr.Row():
-        with gr.Column():
+        with gr.Column(scale=3):
             prompt_input = gr.Textbox(
                 label="Describe your image",
-                placeholder="A traditional Ethiopian market scene...",
-                lines=3
+                placeholder="A futuristic Ethiopian city with flying cars...",
+                lines=3,
+                max_lines=5
             )
-            generate_btn = gr.Button("Generate", variant="primary")
+            with gr.Row():
+                generate_btn = gr.Button("Generate Image", variant="primary")
+                clear_btn = gr.Button("Clear")
             
             gr.Examples(
                 examples=[
-                    ["Habesha cultural dress with gold embroidery, studio lighting"],
-                    ["Lalibela churches at sunrise, foggy morning"],
-                    ["Futuristic Addis Ababa with Ethiopian architecture"]
+                    ["An ancient Aksumite warrior in cyberpunk armor, 4k detailed"],
+                    ["Traditional Ethiopian coffee ceremony in zero gravity"],
+                    ["Portrait of a Habesha queen with golden jewelry"]
                 ],
                 inputs=prompt_input
             )
             
-        with gr.Column():
+        with gr.Column(scale=2):
             output_image = gr.Image(
                 label="Generated Image",
                 type="pil",
-                format="webp",
-                height=400
+                format="png",
+                height=512
             )
             status_output = gr.Textbox(
                 label="Status",
@@ -99,11 +137,15 @@ with gr.Blocks(title="SelamGPT Pro") as demo:
     generate_btn.click(
         fn=generate_image,
         inputs=prompt_input,
+        outputs=[output_image, status_output],
+        queue=True
+    )
+    
+    clear_btn.click(
+        fn=lambda: [None, ""],
         outputs=[output_image, status_output]
     )
 
 if __name__ == "__main__":
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860
-    )
+    demo.queue(max_size=2)
+    demo.launch(server_name="0.0.0.0", server_port=7860)