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app.py

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+import gradio as gr
+import torch
+import cv2
+import numpy as np
+import torchvision.transforms as transforms
+from PIL import Image, ImageEnhance
+from torchvision import models
+import torch.nn as nn
+import matplotlib.pyplot as plt
+import torch.nn.functional as F
+import ssl
+import certifi
+import os
+
+ssl._create_default_https_context = lambda: ssl.create_default_context(cafile=certifi.where())
+
+# Set device
+device = "cpu"
+
+# Number of classes
+num_classes = 6
+
+# Load the pre-trained ResNet model
+model = models.resnet152(pretrained=True)
+for param in model.parameters():
+    param.requires_grad = False  # Freeze feature extractor
+
+# Modify the classifier for 6 classes with an additional hidden layer
+model.fc = nn.Sequential(
+    nn.Linear(model.fc.in_features, 512),
+    nn.ReLU(),
+    nn.Linear(512, num_classes)
+)
+
+# Load trained weights
+model.load_state_dict(torch.load('model.pth', map_location=torch.device('cpu')))
+model.eval()
+
+# Class labels
+class_labels = ['bird', 'cat', 'deer', 'dog', 'frog', 'horse']
+
+# Image transformation function
+def transform_image(image):
+    """Preprocess the input image."""
+    transform = transforms.Compose([
+        transforms.Resize((32, 32)),
+        transforms.ToTensor(),
+        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+    ])
+    img_tensor = transform(image).unsqueeze(0).to(device)
+    return img_tensor
+
+# Apply feature filters
+def apply_filters(image, brightness, contrast, hue):
+    """Adjust Brightness, Contrast, and Hue of the input image."""
+    image = image.convert("RGB")  # Ensure RGB mode
+
+    # Adjust brightness
+    enhancer = ImageEnhance.Brightness(image)
+    image = enhancer.enhance(brightness)
+
+    # Adjust contrast
+    enhancer = ImageEnhance.Contrast(image)
+    image = enhancer.enhance(contrast)
+
+    # Adjust hue (convert to HSV, modify, and convert back)
+    image = np.array(image)
+    hsv_image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV).astype(np.float32)
+    hsv_image[..., 0] = (hsv_image[..., 0] + hue * 180) % 180  # Adjust hue
+    image = cv2.cvtColor(hsv_image.astype(np.uint8), cv2.COLOR_HSV2RGB)
+
+    return Image.fromarray(image)
+
+# Superimposition function
+def superimpose_images(base_image, overlay_image, alpha):
+    """Superimpose overlay_image onto base_image with a given alpha blend."""
+    if overlay_image is None:
+        return base_image  # No overlay, return base image as is
+
+    # Resize overlay image to match base image
+    overlay_image = overlay_image.resize(base_image.size)
+
+    # Convert to numpy arrays
+    base_array = np.array(base_image).astype(float)
+    overlay_array = np.array(overlay_image).astype(float)
+
+    # Blend images
+    blended_array = (1 - alpha) * base_array + alpha * overlay_array
+    blended_array = np.clip(blended_array, 0, 255).astype(np.uint8)
+
+    return Image.fromarray(blended_array)
+
+# Prediction function
+def predict(image, brightness, contrast, hue, overlay_image, alpha):
+    """Apply filters, superimpose, classify image, and visualize results."""
+    if image is None:
+        return None, None, None
+
+    # Apply feature filters
+    processed_image = apply_filters(image, brightness, contrast, hue)
+
+    # Superimpose overlay image
+    final_image = superimpose_images(processed_image, overlay_image, alpha)
+
+    # Convert PIL Image to Tensor
+    image_tensor = transform_image(final_image)
+
+    with torch.no_grad():
+        output = model(image_tensor)
+        probabilities = F.softmax(output, dim=1).cpu().numpy()[0]
+
+    # Generate Bar Chart
+    with plt.xkcd():
+        fig, ax = plt.subplots(figsize=(5, 3))
+        ax.bar(class_labels, probabilities, color='skyblue')
+        ax.set_ylabel("Probability")
+        ax.set_title("Class Probabilities")
+        ax.set_ylim([0, 1])
+        for i, v in enumerate(probabilities):
+            ax.text(i, v + 0.02, f"{v:.2f}", ha='center', fontsize=10)
+
+    return final_image, fig
+
+# Gradio Interface
+with gr.Blocks() as interface:
+    gr.Markdown("<h2 style='text-align: center;'>Image Classifier with Superimposition & Adjustable Filters</h2>")
+    
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="pil", label="Upload Base Image")
+            overlay_input = gr.Image(type="pil", label="Upload Overlay Image (Optional)")
+            brightness = gr.Slider(0.5, 2.0, value=1.0, label="Brightness")
+            contrast = gr.Slider(0.5, 2.0, value=1.0, label="Contrast")
+            hue = gr.Slider(-0.5, 0.5, value=0.0, label="Hue")
+            alpha = gr.Slider(0.0, 1.0, value=0.5, label="Overlay Weight (Alpha)")
+
+        with gr.Column():
+            processed_image = gr.Image(label="Final Processed Image")
+            bar_chart = gr.Plot(label="Class Probabilities")
+
+    inputs = [image_input, brightness, contrast, hue, overlay_input, alpha]
+    outputs = [processed_image, bar_chart]
+
+    # Event listeners for real-time updates
+    image_input.change(predict, inputs=inputs, outputs=outputs)
+    overlay_input.change(predict, inputs=inputs, outputs=outputs)
+    brightness.change(predict, inputs=inputs, outputs=outputs)
+    contrast.change(predict, inputs=inputs, outputs=outputs)
+    hue.change(predict, inputs=inputs, outputs=outputs)
+    alpha.change(predict, inputs=inputs, outputs=outputs)
+
+interface.launch()

model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:76febe50dff559d23c30d9ef496bbb3fa3c5cbe8e75b7086660efd2b1addb09a
+size 237644690

requirements.txt

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+aiofiles==23.2.1
+annotated-types==0.7.0
+anyio==4.8.0
+certifi==2025.1.31
+charset-normalizer==3.4.1
+click==8.1.8
+fastapi==0.115.8
+ffmpy==0.5.0
+filelock==3.17.0
+fsspec==2025.2.0
+gradio==5.15.0
+gradio_client==1.7.0
+h11==0.14.0
+httpcore==1.0.7
+httpx==0.28.1
+huggingface-hub==0.28.1
+idna==3.10
+Jinja2==3.1.5
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+mdurl==0.1.2
+mpmath==1.3.0
+networkx==3.4.2
+numpy==2.2.2
+opencv-python==4.11.0.86
+orjson==3.10.15
+packaging==24.2
+pandas==2.2.3
+pillow==11.1.0
+pydantic==2.10.6
+pydantic_core==2.27.2
+pydub==0.25.1
+Pygments==2.19.1
+python-dateutil==2.9.0.post0
+python-multipart==0.0.20
+pytz==2025.1
+PyYAML==6.0.2
+requests==2.32.3
+rich==13.9.4
+ruff==0.9.5
+safehttpx==0.1.6
+semantic-version==2.10.0
+setuptools==75.8.0
+shellingham==1.5.4
+six==1.17.0
+sniffio==1.3.1
+starlette==0.45.3
+sympy==1.13.1
+tomlkit==0.13.2
+torch==2.6.0
+torchvision==0.21.0
+tqdm==4.67.1
+typer==0.15.1
+typing_extensions==4.12.2
+tzdata==2025.1
+urllib3==2.3.0
+uvicorn==0.34.0
+websockets==14.2