Create app.py

app.py

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+
+import subprocess
+import sys
+
+# ✅ Function to install missing packages efficiently
+def install(package):
+    try:
+        __import__(package.split("==")[0])  # Try to import package before installing
+    except ImportError:
+        subprocess.run([sys.executable, "-m", "pip", "install", package])
+
+# ✅ List of dependencies to install
+dependencies = [
+    "torch>=2.0.0",
+    "torchvision>=0.15.0",
+    "transformers",
+    "gradio",
+    "pillow",
+    "pandas",
+    "opencv-python-headless",
+    "scikit-learn==1.3.0"
+]
+
+# ✅ Install dependencies
+for package in dependencies:
+    install(package)
+import subprocess
+import sys
+
+# ✅ Function to install missing packages
+def install(package):
+    subprocess.check_call([sys.executable, "-m", "pip", "install", package])
+
+# ✅ Ensure required libraries are installed
+for package in ["torch", "torchvision", "transformers", "gradio", "pillow", "pandas", "opencv-python", "scikit-learn"]:
+    try:
+        __import__(package)
+    except ImportError:
+        install(package)
+
+# ✅ Import libraries after installation
+import torch
+import torch.nn as nn
+import torchvision.transforms as transforms
+import torchvision.models as models
+import pandas as pd
+from PIL import Image
+import gradio as gr
+from sklearn.preprocessing import LabelEncoder
+
+# ✅ Load metadata
+CSV_PATH = "HAM10000_metadata.csv"
+DATA_PATH = "ham10000_images/"
+
+df = pd.read_csv(CSV_PATH)
+label_encoder = LabelEncoder()
+df["label"] = label_encoder.fit_transform(df["dx"])  # Convert labels to numbers
+classes = label_encoder.classes_  # Get class names
+
+# ✅ Define image transformation
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+])
+
+# ✅ Load a pre-trained EfficientNet model
+model = models.efficientnet_b0(pretrained=True)
+num_features = model.classifier[1].in_features
+model.classifier[1] = nn.Linear(num_features, len(classes))  # Adjust for 7 classes
+model.load_state_dict(torch.load("ham10000_model.pth", map_location=torch.device('cpu')))
+model.eval()
+
+# ✅ Function to classify skin disease
+def classify_skin_disease(image):
+    image = Image.fromarray(image)  # Convert to PIL image
+    image = transform(image).unsqueeze(0)  # Apply transformations
+
+    with torch.no_grad():
+        output = model(image)
+        probabilities = torch.nn.functional.softmax(output[0], dim=0)
+    
+    # Convert probabilities to dictionary
+    results = {classes[i]: f"{probabilities[i].item():.2%}" for i in range(len(classes))}
+    return results
+
+# ✅ Create Gradio Interface
+iface = gr.Interface(
+    fn=classify_skin_disease,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(num_top_classes=3),
+    title="🩺 AI Skin Disease Classifier",
+    description="📷 Upload a skin lesion image and the model will classify it.",
+    examples=["example_eczema.jpg", "example_melanoma.jpg"],  # Add sample images
+)
+
+# ✅ Run Gradio App
+if __name__ == "__main__":
+    iface.launch()