Update app.py

app.py

@@ -1,36 +1,54 @@
 import gradio as gr
-import requests
-import base64
 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")
 
 
-API_URL = "https://api-inference.huggingface.co/models/facebook/detr-resnet-50-panoptic"
-headers = {"Authorization": "Bearer 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"}
+# 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
 
-inputs = gr.inputs.Image(type="pil", label="Upload an image")
-# output = query("cats.jpg")
+# 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
 
 
-# Perform image segmentation for multy class output
-def query(inputs):
-    # with open(inputs, "rb") as f:
-    #     data = f.read()
-    response = requests.post(API_URL, headers=headers, data=base64.b64encode(inputs))
-    return response.json()
+# 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 = query(inputs)
+outputs = gr.outputs.Label(num_top_classes=4)
 
-title = "<h1 style='text-align: center;'>Image Segmentation</h1>"
-description = "Upload an image and get the segmentation result."
+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=query, 
+gr.Interface(fn=classify_image, 
              inputs=inputs, 
-             outputs=gr.outputs.HTML(), 
+             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()