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app.py

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+#Umut Ozdemir - FastBook - 02_production
+
+# Import the necessary libraries
+import gradio as gr
+from fastai.vision.all import *
+
+# Load your pre-trained model (replace 'export.pkl' with your model's path)
+learn = load_learner('export.pkl')
+
+# Define a function to classify an input image
+def classify_image(input_img):
+    # Load and classify the input image
+    img = PILImage.create(input_img)
+    pred, pred_idx, probs = learn.predict(img)
+    
+    # Format the results as labels and probabilities
+    labels = learn.dls.vocab
+    results = {labels[i]: float(probs[i]) for i in range(len(labels))}
+    
+    return results
+
+# Create a Gradio interface
+demo = gr.Interface(
+    fn=classify_image,           # Function to process input
+    inputs="image",              # Input type is an image
+    outputs="text"               # Output type is text (labels and probabilities)
+)
+
+# Launch the Gradio interface for image classification
+demo.launch()

requirements.txt

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+fastai
+torch
+gradio
+numpy
+pandas <2.0.0