Update app.py

app.py

@@ -1,54 +1,9 @@
-import gradio as gr
-from PIL import Image
-from transformers import AutoFeatureExtractor, AutoModelForImageSegmentation
-import tensorflow as tf
-
-extractor = AutoFeatureExtractor.from_pretrained("facebook/detr-resnet-50-panoptic")
-model = AutoModelForImageSegmentation.from_pretrained("facebook/detr-resnet-50-panoptic")
-
-
-# Perform image classification for single class output
-# def predict_class(image):
-#     img = tf.cast(image, tf.float32)
-#     img = tf.image.resize(img, [input_shape[0], input_shape[1]])
-#     img = tf.expand_dims(img, axis=0)
-#     prediction = model.predict(img)
-#     class_index = tf.argmax(prediction[0]).numpy()
-#     predicted_class = labels[class_index]
-#     return predicted_class
-
-# Perform image classification for multy class output
-def predict_class(image):
-    img = tf.cast(image, tf.float32)
-    prediction = model.predict(img)
-    return prediction
-
-# UI Design for single class output
-# def classify_image(image):
-#     predicted_class = predict_class(image)
-#     output = f"<h2>Predicted Class: <span style='text-transform:uppercase';>{predicted_class}</span></h2>"
-#     return output
-
-
-# UI Design for multy class output
-def classify_image(image):
-    results = predict_class(image)
-    
-    return results
-
-
-inputs = gr.inputs.Image(type="pil", label="Upload an image")
-# outputs = gr.outputs.HTML() #uncomment for single class output 
-outputs = gr.outputs.Label(num_top_classes=4)
-
-title = "<h1 style='text-align: center;'>Image Classifier</h1>"
-description = "Upload an image and get the predicted class."
-# css_code='body{background-image:url("file=wave.mp4");}'
-
-gr.Interface(fn=classify_image, 
-             inputs=inputs, 
-             outputs=outputs, 
-             title=title, 
-             examples=[["00_plane.jpg"], ["01_car.jpg"], ["02_bird.jpg"], ["03_cat.jpg"], ["04_deer.jpg"]],
-             # css=css_code,
-             description=description).launch()
+import json
+import requests
+API_URL = "https://api-inference.huggingface.co/models/gpt2"
+headers = {"Authorization": f"Bearer {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}"}
+def query(payload):
+    data = json.dumps(payload)
+    response = requests.request("POST", API_URL, headers=headers, data=data)
+    return json.loads(response.content.decode("utf-8"))
+data = query("Can you please let us know more details about your ")