Update app.py

app.py

@@ -1,85 +1,48 @@
+import os
 import gradio as gr
-import numpy as np
-import random
-import torch
-from diffusers import DiffusionPipeline
+from google import genai
+from google.genai import types
 
-# Ensure the model runs on CPU
-device = "cpu"
-dtype = torch.float32  # Use float32 for CPU compatibility
+# Set your Google API key
+os.environ["GOOGLE_API_KEY"] = "AIzaSyDL5Rilo7ptJpUOZdY6wy8PJYUcVcnDADs"
 
-# Load model from Hugging Face (it will cache locally in Hugging Face Spaces)
-pipe = DiffusionPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-schnell", 
-    torch_dtype=dtype, 
-    low_cpu_mem_usage=True
-).to(device)
+# Initialize the Google Generative AI client
+client = genai.Client(api_key=os.environ["GOOGLE_API_KEY"])
 
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
+MODEL_NAME = "gemini-2.5-flash-image-preview"
 
-def infer(prompt, seed=42, randomize_seed=False, width=512, height=512, num_inference_steps=4):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    generator = torch.Generator(device=device).manual_seed(seed)
-    image = pipe(
-        prompt=prompt,
-        width=width,
-        height=height,
-        num_inference_steps=num_inference_steps,
-        generator=generator,
-        guidance_scale=0.0
-    ).images[0]
-    return image, seed
+def remix_images(image1, image2, prompt):
+    try:
+        # Run request to Google Generative AI
+        response = client.models.generate_images(
+            model=MODEL_NAME,
+            prompt=prompt,
+            images=[image1, image2]
+        )
 
-examples = [
-    "a tiny astronaut hatching from an egg on the moon",
-    "a cat holding a sign that says hello world",
-    "an anime illustration of a wiener schnitzel",
-]
+        # Extract the first generated image
+        if response.generated_images and len(response.generated_images) > 0:
+            img = response.generated_images[0]
+            return img.image  # returns PIL.Image
+        else:
+            return None
+    except Exception as e:
+        return f"Error: {str(e)}"
 
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 520px;
-}
-"""
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("## 🖼️ Google Gemini 2.5 Flash Image Preview (via Gradio)")
 
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown("""# FLUX.1 [schnell]
-        12B param rectified flow transformer distilled from FLUX.1 [pro]
-        """)
-        
-        with gr.Row():
-            prompt = gr.Text(label="Prompt", show_label=False, max_lines=1, placeholder="Enter your prompt", container=False)
-            run_button = gr.Button("Run", scale=0)
-        
-        result = gr.Image(label="Result", show_label=False)
-        
-        with gr.Accordion("Advanced Settings", open=False):
-            seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
-            with gr.Row():
-                width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512)
-                height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512)
-            
-            num_inference_steps = gr.Slider(label="Number of inference steps", minimum=1, maximum=50, step=1, value=4)
-        
-        gr.Examples(
-            examples=examples,
-            fn=infer,
-            inputs=[prompt],
-            outputs=[result, seed],
-            cache_examples="lazy"
-        )
-    
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[prompt, seed, randomize_seed, width, height, num_inference_steps],
-        outputs=[result, seed]
-    )
+    with gr.Row():
+        img1 = gr.Image(type="filepath", label="Upload Image 1")
+        img2 = gr.Image(type="filepath", label="Upload Image 2")
+
+    prompt = gr.Textbox(label="Prompt", placeholder="Describe how to remix the images...")
+
+    btn = gr.Button("Generate")
+    output = gr.Image(label="Generated Image")
+
+    btn.click(fn=remix_images, inputs=[img1, img2, prompt], outputs=output)
 
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()