Create app.py

app.py

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+import gradio as gr
+from fastai.learner import load_learner
+from fastai.vision.all import *
+import matplotlib.pyplot as plt
+
+# Load the trained model
+learn = load_learner('emotion_model')
+
+# Define function to preprocess image
+def preprocess_image(image):
+    # Convert image to PILImage
+    image_pil = PILImage.create(image)
+    # Apply same transformations as done during training
+    image_resized = Resize(224)(image_pil)
+    return image_resized
+
+# Define function to predict emotion and generate confidence bar chart
+def predict_emotion(image):
+    # Preprocess the image
+    processed_image = preprocess_image(image)
+    # Predict the emotion
+    pred_class, pred_idx, outputs = learn.predict(processed_image)
+    # Get class names
+    class_names = learn.dls.vocab
+    # Generate confidence values
+    confidences = [outputs[idx].item() for idx in range(len(outputs))]
+    # Generate bar chart
+    plt.bar(class_names, confidences)
+    plt.xlabel('Emotion')
+    plt.ylabel('Confidence')
+    plt.title('Confidence of Predicted Emotions')
+    plt.xticks(rotation=45)
+    plt.tight_layout()
+    return plt
+
+# Define Gradio interface
+iface = gr.Interface(
+    fn=predict_emotion,
+    inputs="image",
+    outputs="plot",
+    title="Emotion Prediction",
+    description="Upload an image and get the confidence of predicted emotions",
+    interpretation="default",
+    live=True
+)
+
+# Launch the interface
+iface.launch()