Main py file with model calls

app.py

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+import gradio as gr
+from google import genai
+from google.genai import types
+from PIL import Image
+from io import BytesIO
+import tempfile
+import concurrent.futures
+
+client = genai.Client(API_KEY)
+PROMPT_VARIATIONS = [
+    "Generate a high quality image with a minimalist white background, bright natural light, and an open lid. Keep the product text unchanged.",
+    "Generate a high quality image of a beautiful woman who is using the product. The product and its text must remain exactly as in the original image.",
+    "Generate a high quality image from a slightly elevated angle, with a modern indoor setting, warm lighting, and an open lid. Ensure the product and product text is preserved.",
+    "Generate a high quality image with a low angle shot, cool ambient lighting, and a contemporary studio background, featuring an open lid. Keep the product text intact.",
+    "Generate a high quality image with a vibrant outdoor background, natural sunlight, and an open lid. The product text should remain the same.",
+    "Generate a high quality image with a subtle gradient background, soft diffused lighting, and an open lid. Ensure the product and product text match the original.",
+    "Generate a high quality image with an artistic abstract background, high contrast lighting, and an open lid. Maintain the original product text.",
+    "Generate a high quality image with a dynamic angle shot, colorful background, balanced lighting, and an open lid. Preserve the product text.",
+    "Generate a high quality image with a scenic nature background, golden hour lighting, and an open lid. Keep the product text unchanged.",
+    "Generate a high quality image where a beautiful woman is using the product. The product and its text must remain exactly as in the original image."
+]
+
+def process_variation(variation, input_image, product_name):
+    """Process one variation: call the API, save the generated image to a temporary PNG file, and return its path."""
+    text_input = (
+        f"Hi, this is a picture of a product. The name of the product is {product_name}.",
+        variation
+    )
+    response = client.models.generate_content(
+        model=univin,  # Ensure this is the correct model identifier.
+        contents=[text_input, input_image],
+        config=types.GenerateContentConfig(response_modalities=['Text', 'Image'])
+    )
+    for part in response.candidates[0].content.parts:
+        if part.inline_data is not None:
+            generated_img = Image.open(BytesIO(part.inline_data.data))
+            with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmp_file:
+                generated_img.save(tmp_file, format="PNG")
+                return tmp_file.name
+    return None
+
+def generate_images(input_image, product_name):
+    """Generate images concurrently for the defined prompt variations (exactly 10 calls)."""
+    with concurrent.futures.ThreadPoolExecutor() as executor:
+        futures = [
+            executor.submit(process_variation, variation, input_image, product_name)
+            for variation in PROMPT_VARIATIONS
+        ]
+        output_files = [future.result() for future in futures if future.result() is not None]
+    return output_files
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Uni-Imaginator")
+    with gr.Row():
+        input_image = gr.Image(type="pil", label="Upload Image")
+        product_name = gr.Textbox(label="Product Name", placeholder="Enter the product name")
+    generate_button = gr.Button("Generate Images")
+    
+    # The Gallery displays file paths so that each image downloads as a PNG file.
+    gallery = gr.Gallery(label="Generated Images", elem_id="gallery", columns=4, height="auto", object_fit="contain", interactive=True)
+    
+    generate_button.click(fn=generate_images, inputs=[input_image, product_name], outputs=gallery)
+    
+demo.launch()