Create app.py

app.py

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+import gradio as gr
+import requests
+import io
+import os
+import tempfile
+from PIL import Image
+import traceback
+
+# API details
+API_URL = "https://api-inference.huggingface.co/models/SaladSlayer00/twin_matcher"
+headers = {"Authorization": f"{os.getenv('TOKEN')}"}
+
+# Function to query the API with an image file
+def query(filename):
+    with open(filename, "rb") as f:
+        data = f.read()
+    response = requests.post(API_URL, headers=headers, data=data)
+    response.raise_for_status()  # Ensure successful response
+    return response.json()
+
+# Function to process the image and predict
+def predict(image):
+    try:
+        with tempfile.NamedTemporaryFile(delete=False, suffix='.jpeg') as tmp_file:
+            image.save(tmp_file, format="JPEG")
+            tmp_file_path = tmp_file.name
+
+        predictions = query(tmp_file_path)
+
+        top_prediction = max(predictions, key=lambda x: x['score'])
+        result = (top_prediction['label'], top_prediction['score'])
+
+        os.remove(tmp_file_path)
+
+        return result
+    except Exception as e:
+        print(f"Exception during prediction: {e}")
+        traceback.print_exc()
+        return "Error during prediction", "N/A"
+
+# Gradio interface
+with gr.Interface(fn=predict, 
+                  inputs=gr.Image(type="pil"), 
+                  outputs=["text", "number"],
+                  title="Celebrity Lookalike Predictor",
+                  description="Take a snapshot to see which celebrity you look like!") as demo:
+    demo.launch()