Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from PIL import Image
+import requests
+from io import BytesIO
+import numpy as np
+
+# Load the pre-trained model and tokenizer
+model_name = "distilbert/distilbert-base-uncased"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Function to preprocess the image
+def preprocess_image(image):
+    image = Image.open(BytesIO(image))
+    image = image.resize((256, 256))  # Resize the image to match the model's input size
+    return np.array(image)
+
+# Function to make predictions
+def classify_image(image):
+    image = preprocess_image(image)
+    inputs = tokenizer(image, return_tensors="pt", padding=True, truncation=True)
+    outputs = model(**inputs)
+    logits = outputs.logits.detach().numpy()[0]
+    probabilities = np.exp(logits) / np.exp(logits).sum(-1)
+    predicted_class = np.argmax(probabilities)
+    return {str(i): float(prob) for i, prob in enumerate(probabilities)}
+
+# Create a Gradio interface
+input_image = gr.inputs.Image(shape=(256, 256))
+output_label = gr.outputs.Label(num_top_classes=3)
+gr.Interface(classify_image, inputs=input_image, outputs=output_label).launch()