Update app.py

app.py

@@ -4,14 +4,13 @@ from PIL import Image
 import numpy as np
 import traceback
 import gradio as gr
-from transformers import DetrImageProcessor, DetrForObjectDetection, LayoutLMTokenizer, LayoutLMForTokenClassification
-from diffusers import StableDiffusionPipeline, StableDiffusionUpscalePipeline
+from transformers import DetrImageProcessor, DetrForObjectDetection
+from diffusers import StableDiffusionPipeline
 from huggingface_hub import login
 import torchvision.transforms as T
-import torchvision.models as models
-from dotenv import load_dotenv
 
 # Load environment variables from .env file
+from dotenv import load_dotenv
 load_dotenv()
 
 # Retrieve Hugging Face token from environment variable
@@ -19,12 +18,12 @@ HF_TOKEN = os.getenv("HF_TOKEN")
 if HF_TOKEN is None:
     raise ValueError("Hugging Face token not found in environment variables.")
 
-## 2.1 Image Analysis with DETR
+# Load DETR model for object detection
 def load_detr_model():
     try:
-        detr_model = DetrForObjectDetection.from_pretrained('facebook/detr-resnet-50')
-        detr_processor = DetrImageProcessor.from_pretrained('facebook/detr-resnet-50')
-        return detr_model, detr_processor, None
+        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)}"
 
@@ -43,72 +42,12 @@ def detect_objects(image):
     else:
         return None, "DETR models not loaded. Skipping object detection."
 
-## 2.2 Style Transfer with Deep Image Prior
-def style_transfer(content_image, style_image):
-    try:
-        transform = T.Compose([
-            T.Resize((512, 512)),
-            T.ToTensor(),
-            T.Lambda(lambda x: x.mul(255))
-        ])
-
-        content = transform(content_image).unsqueeze(0).requires_grad_(False)
-        style = transform(style_image).unsqueeze(0).requires_grad_(False)
-
-        vgg = models.vgg19(pretrained=True).features.eval()
-        for param in vgg.parameters():
-            param.requires_grad_(False)
-
-        generated = content.clone().requires_grad_(True)
-        optimizer = torch.optim.Adam([generated], lr=0.003)
-
-        for i in range(300):
-            generated_features = vgg(generated)
-            content_features = vgg(content)
-            style_features = vgg(style)
-
-            content_loss = torch.mean((generated_features - content_features)**2)
-            style_loss = torch.mean((generated_features - style_features)**2)
-
-            total_loss = content_loss + style_loss
-            optimizer.zero_grad()
-            total_loss.backward()
-            optimizer.step()
-
-        generated_image = generated.squeeze().clamp(0, 255).cpu().detach().numpy().transpose(1, 2, 0)
-        return Image.fromarray(np.uint8(generated_image)), None
-    except Exception as e:
-        return content_image, f"Error in style_transfer: {str(e)}"
-
-## 2.3 Layout Generation with LayoutLM
-def load_layoutlm_model():
-    try:
-        layoutlm_tokenizer = LayoutLMTokenizer.from_pretrained('microsoft/layoutlm-base-uncased')
-        layoutlm_model = LayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased')
-        return layoutlm_tokenizer, layoutlm_model, None
-    except Exception as e:
-        return None, None, f"Error loading LayoutLM model: {str(e)}"
-
-layoutlm_tokenizer, layoutlm_model, layoutlm_error = load_layoutlm_model()
-
-def generate_layout(text):
-    if layoutlm_tokenizer is not None and layoutlm_model is not None:
-        try:
-            inputs = layoutlm_tokenizer(text, return_tensors="pt")
-            outputs = layoutlm_model(**inputs)
-            layout = outputs.logits.argmax(dim=-1)
-            return layout, None
-        except Exception as e:
-            return None, f"Error in generate_layout: {str(e)}"
-    else:
-        return None, "LayoutLM models not loaded. Skipping layout generation."
-
-## 2.4 Image Generation with Stable Diffusion
+# Load Stable Diffusion model for image generation
 def load_stable_diffusion_model():
     try:
         login(token=HF_TOKEN)
-        sd_pipeline = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4").to("cuda")
-        return sd_pipeline, None
+        pipeline = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4").to("cuda")
+        return pipeline, None
     except Exception as e:
         return None, f"Error loading Stable Diffusion model: {str(e)}"
 
@@ -124,69 +63,32 @@ def generate_image(prompt):
     else:
         return None, "Stable Diffusion model not loaded. Skipping image generation."
 
-## 2.5 Super-Resolution
-def load_upscale_pipeline():
-    try:
-        upscale_pipeline = StableDiffusionUpscalePipeline.from_pretrained("stabilityai/stable-diffusion-x4-upscaler").to("cuda")
-        return upscale_pipeline, None
-    except Exception as e:
-        return None, f"Error loading Upscale Pipeline: {str(e)}"
-
-upscale_pipeline, upscale_error = load_upscale_pipeline()
-
-def super_resolve(image):
-    if upscale_pipeline is not None:
-        try:
-            if not isinstance(image, Image.Image):
-                raise ValueError("Input must be a PIL image.")
-            upscaled_image = upscale_pipeline(image=image).images[0]
-            return upscaled_image, None
-        except Exception as e:
-            return None, f"Error in super_resolve: {str(e)}"
-    else:
-        return image, "Upscale Pipeline not loaded. Skipping super-resolution."
-
-# Step 3: Gradio Interface and Integration
-def process_image(image, style_image, text_prompt):
+# Gradio Interface
+def process_image(image):
     try:
         # Detect objects
         object_results, detect_error = detect_objects(image)
         if detect_error:
             return None, detect_error
 
-        # Style transfer
-        styled_image, style_error = style_transfer(image, style_image)
-        if style_error:
-            return None, style_error
-
-        # Generate layout
-        layout_results, layout_error = generate_layout(text_prompt)
-        if layout_error:
-            return None, layout_error
-
-        # Generate image based on layout
-        generated_image, gen_image_error = generate_image("modern interior design based on layout")
+        # Generate a modern redesign of the image based on the detected objects
+        # For simplicity, we'll use a fixed prompt for image generation
+        prompt = "modern redesign of an interior room"
+        generated_image, gen_image_error = generate_image(prompt)
         if gen_image_error:
             return None, gen_image_error
 
-        # Super-resolve the generated image
-        final_image, upscale_error = super_resolve(generated_image)
-        if upscale_error:
-            return None, upscale_error
-
-        return final_image, None
+        return generated_image, None
     except Exception as e:
         return None, f"Error in process_image: {str(e)}"
 
 iface = gr.Interface(
     fn=process_image,
     inputs=[
-        gr.Image(type="pil", label="Upload Room Image"),
-        gr.Image(type="pil", label="Upload Style Image"),
-        gr.Textbox(label="Enter Design Prompt")
+        gr.Image(type="pil", label="Upload Room Image")
     ],
     outputs=[
-        gr.Image(type="pil", label="Generated Image"),
+        gr.Image(type="pil", label="Redesigned Image"),
         gr.Textbox(label="Error Message")
     ]
 )
@@ -195,4 +97,4 @@ try:
     iface.launch()
 except Exception as e:
     print(f"Error occurred while launching the interface: {str(e)}")
-    traceback.print_exc()
+    traceback.print_exc()