Update app.py

app.py

@@ -1,140 +1 @@
-import os
-import torch
-from PIL import Image
-import gradio as gr
-from transformers import DetrImageProcessor, DetrForObjectDetection
-from diffusers import StableDiffusionPipeline
-from huggingface_hub import login
-from dotenv import load_dotenv
-
-# Load environment variables from .env file
-load_dotenv()
-
-# Retrieve Hugging Face token from environment variable
-HF_TOKEN = os.getenv('HF_TOKEN')
-
-if HF_TOKEN is None:
-    raise ValueError("Hugging Face token not found in environment variables.")
-
-# Login to Hugging Face using the token
-login(token=HF_TOKEN)
-
-# Load DETR model for object detection
-def load_detr_model():
-    try:
-        model = DetrForObjectDetection.from_pretrained('facebook/detr-resnet-50')
-        processor = DetrImageProcessor.from_pretrained('facebook/detr-resnet-50')
-        return model, processor, None
-    except Exception as e:
-        return None, None, f"Error loading DETR model: {str(e)}"
-
-detr_model, detr_processor, detr_error = load_detr_model()
-
-def detect_objects(image):
-    if image is None:
-        return None, "Invalid image: image is None."
-
-    if detr_model is not None and detr_processor is not None:
-        try:
-            inputs = detr_processor(images=image, return_tensors="pt")
-            outputs = detr_model(**inputs)
-            target_sizes = torch.tensor([image.size[::-1]])
-            results = detr_processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
-
-            detected_objects = [
-                {"label": detr_model.config.id2label[label.item()],
-                 "box": box.tolist()}
-                for label, box in zip(results['labels'], results['boxes'])
-            ]
-            return detected_objects, None
-        except Exception as e:
-            return None, f"Error in detect_objects: {str(e)}"
-    else:
-        return None, "DETR models not loaded. Skipping object detection."
-
-# Load Stable Diffusion model for image generation
-def load_stable_diffusion_model():
-    try:
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-        pipeline = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4").to(device)
-        return pipeline, None
-    except Exception as e:
-        return None, f"Error loading Stable Diffusion model: {str(e)}"
-
-sd_pipeline, sd_error = load_stable_diffusion_model()
-
-def adjust_dimensions(width, height):
-    # Adjust width and height to be divisible by 8
-    adjusted_width = (width // 8) * 8
-    adjusted_height = (height // 8) * 8
-    return adjusted_width, adjusted_height
-
-def generate_image(prompt, width, height):
-    if sd_pipeline is not None:
-        try:
-            adjusted_width, adjusted_height = adjust_dimensions(width, height)
-            image = sd_pipeline(prompt, width=adjusted_width, height=adjusted_height).images[0]
-            # Resize back to original dimensions if needed
-            image = image.resize((width, height), Image.LANCZOS)
-            return image, None
-        except Exception as e:
-            return None, f"Error in generate_image: {str(e)}"
-    else:
-        return None, "Stable Diffusion model not loaded. Skipping image generation."
-
-def process_image(image):
-    if image is None:
-        return None, "Invalid image: image is None."
-
-    try:
-        # Detect objects in the provided image
-        detected_objects, detect_error = detect_objects(image)
-        if detect_error:
-            return None, detect_error
-
-        # Create a prompt based on detected objects
-        prompt = "modern redesign of an interior room with "
-        if detected_objects:
-            prompt += ", ".join([obj['label'] for obj in detected_objects])
-        else:
-            prompt += "empty room"
-
-        # Generate a redesigned image based on the prompt
-        width, height = image.size
-        generated_image, gen_image_error = generate_image(prompt, width, height)
-        if gen_image_error:
-            return None, gen_image_error
-
-        return generated_image, None
-    except Exception as e:
-        return None, f"Error in process_image: {str(e)}"
-
-# Custom CSS for styling
-custom_css = """
-body {
-    background-color: black;
-}
-
-h1 {
-    background: linear-gradient(to right, blue, purple);
-    -webkit-background-clip: text;
-    color: transparent;
-    font-size: 3em;
-    text-align: center;
-    margin-bottom: 20px;
-}
-"""
-
-# Creating the Gradio interface with custom styling
-iface = gr.Interface(
-    fn=process_image,
-    inputs=[gr.Image(type="pil", label="Upload Room Image")],
-    outputs=[gr.Image(type="pil", label="Redesigned Image"), gr.Textbox(label="Error Message")],
-    title="Interior Redesign",
-    css=custom_css
-)
-
-try:
-    iface.launch()
-except Exception as e:
-    print(f"Error occurred while launching the interface: {str(e)}")
+print("hello")