Create app.py

app.py

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+import torch
+from PIL import Image
+from diffusers import DiffusionPipeline
+import gradio as gr
+import google.generativeai as genai
+import os 
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Access the API key from the environment
+GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
+
+# Error handling (optional)
+if not GOOGLE_API_KEY:
+    raise ValueError("Missing GOOGLE_API_KEY environment variable. Please set it in your .env file.")
+
+# Configure the genai library
+genai.configure(api_key=GOOGLE_API_KEY)
+
+# Initialize Gemini models
+model1 = genai.GenerativeModel('gemini-1.0-pro-latest')
+model2 = genai.GenerativeModel('gemini-1.5-flash-latest')
+
+# Define the function to transform images
+
+model_path = "GiantAnalytics/sdxl_fine_tuned_model_aditya_2"
+pipe = DiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16)
+
+# Set the device based on CUDA availability
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pipe.to(device)
+
+def enhance_prompt_and_generate_images(image, prompt):
+    if isinstance(image, np.ndarray):
+        image = Image.fromarray(image.astype('uint8'), 'RGB')
+    try:
+      prompt11='''provide me all the information about texture of the design how it is looking and design of the input textile image in descriptive format
+        It should provide like this Texture Details: , Design Details: and overall description of image'''
+        # Step 1: Get an enhanced prompt using the Gemini API
+        response1 = model2.generate_content([prompt11, image], stream=False)
+        response1.resolve()
+        initial_description = response1.text
+
+        if initial_description:
+            enhanced_prompt = f'''First, identify the user's specifications provided in the prompt: {user_input}.
+        Understand the image details: {initial_description}. Now, generate a detailed prompt that combines the user inputs with the image details in a suitable way.
+        This new prompt will help generate a new image with the SDXL model. The prompt should be concise and less than 100 tokens; curate it carefully.
+        Focus on maintaining the theme and the overall feel of the design, incorporating subtle changes that enhance its uniqueness and visual appeal.'''
+            response2 = model1.generate_content([enhanced_prompt], stream=False)
+            response2.resolve()
+            final_prompt = response2.text if response2.text else prompt
+        else:
+            final_prompt = prompt 
+            print(final_prompt) # Use original prompt if no description is available
+
+    except Exception as e:
+        print(f"Failed to enhance prompt via Gemini API: {e}")
+        final_prompt = prompt  # Use original prompt on any error
+
+    # Step 2: Generate three image variations
+    image_variations = []
+    settings = [(7.5, 0.5), (8.0, 0.6), (6.0, 0.4)]  # Custom settings for guidance_scale and strength
+    for i, (guidance, strength) in enumerate(settings):  # Different settings for variations
+        generator = torch.Generator(device=device).manual_seed(i * 100)
+        output = pipe(prompt=final_prompt, image=image, guidance_scale=guidance, strength=strength, generator=generator).images[0]
+        image_variations.append(output)
+
+    return image_variations
+
+# Path to your local logo image
+logo_path = '/content/RCD-Final Logosmall size.jpg'  # Replace with your image path
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column(scale=10):
+            gr.Markdown(
+                """
+                <div id="logo-container">
+                    <h1>Text Guided Image-to-Image Generation</h1>
+                    <p>Enter a text prompt with required parameters to transform the Input Image using the Fine-Tuned SDXL Model.</p>
+                </div>
+                """,
+                elem_id="logo-container"
+            )
+        with gr.Column(scale=1, elem_id="logo-column"):
+            logo = gr.Image(value=logo_path, elem_id="logo", height=128, width=128)
+
+    with gr.Row():
+        img_input = gr.Image(label="Upload Image")
+        prompt_input = gr.Textbox(label="Enter your prompt")
+        submit_btn = gr.Button("Generate")
+
+    with gr.Row():
+        output_image1 = gr.Image(label="Variation 1")
+        output_image2 = gr.Image(label="Variation 2")
+        output_image3 = gr.Image(label="Variation 3")
+
+    submit_btn.click(
+        enhance_prompt_and_generate_images,
+        inputs=[img_input, prompt_input],
+        outputs=[output_image1, output_image2, output_image3]
+    )
+
+if __name__ == "__main__":
+    demo.launch(debug=True)#inline=False)