Create app.py

app.py

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+import gradio as gr
+import torch
+from torchvision import models, transforms
+from PIL import Image
+import io
+
+# Load the pre-trained model
+model = models.resnet50(pretrained=False)
+model.fc = torch.nn.Linear(2048, 7)  # Adjust for the number of emotion categories
+model.load_state_dict(torch.load('model/emotion_model.pth'))
+model.eval()
+
+# Define image transforms
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+# Emotion classes (adjust based on your dataset)
+emotions = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']
+
+# Define the prediction function
+def predict_emotion(image):
+    img = Image.open(io.BytesIO(image))
+    img = transform(img).unsqueeze(0)  # Add batch dimension
+
+    with torch.no_grad():
+        output = model(img)
+        probs = torch.nn.functional.softmax(output, dim=1)
+        confidence, predicted_class = probs.max(1)
+
+    emotion = emotions[predicted_class.item()]
+    percentage = confidence.item() * 100
+    return emotion, f"{percentage:.2f}%"
+
+# Set up the Gradio interface
+iface = gr.Interface(
+    fn=predict_emotion,
+    inputs=gr.inputs.Image(type="bytes"),
+    outputs=[gr.outputs.Textbox(label="Predicted Emotion"), gr.outputs.Textbox(label="Confidence")],
+    live=True,
+    title="Emotion Classification",
+    description="Upload an image to predict the emotion expressed in the image using a fine-tuned ResNet50 model."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()