Create app.py

app.py

@@ -0,0 +1,36 @@
+import os
+import gradio as gr
+from PIL import Image
+import requests
+
+# Get the Hugging Face API token from the environment variable
+HF_API_TOKEN = os.getenv("HF_API_TOKEN")
+HF_API_URL = "https://api-inference.huggingface.co/models/google/vit-base-patch16-224"
+
+def predict(image):
+    """
+    Sends the image to the Hugging Face model API for prediction.
+    """
+    headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
+    # Convert the image to a format accepted by the model
+    payload = {"inputs": image}
+    
+    try:
+        response = requests.post(HF_API_URL, headers=headers, json=payload)
+        response.raise_for_status()
+        return response.json()
+    except requests.exceptions.RequestException as e:
+        return f"Error: {e}"
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="filepath"),  # Accepts an image file
+    outputs="text",                   # Outputs the prediction result as text
+    title="Hugging Face Camera Integration",
+    description="Upload an image to get predictions from the Hugging Face model."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()