Upload app.py

app.py

@@ -1,37 +1,76 @@
-# import gradio as gr
-
-# demo = gr.load("AZIIIIIIIIZ/vit-base-patch16-224-finetuned-eurosat", src="models")
-
-# demo.launch()
-
-###########################33
 
 import gradio as gr
 
 # Use a pipeline as a high-level helper
 from transformers import pipeline
 
-pipe = pipeline("image-classification", model="AZIIIIIIIIZ/vit-base-patch16-224-finetuned-eurosat")
 # Use a pipeline as a high-level helper
 # Load model directly
 from transformers import AutoImageProcessor, AutoModelForImageClassification
 
-processor = AutoImageProcessor.from_pretrained("AZIIIIIIIIZ/vit-base-patch16-224-finetuned-eurosat")
-model = AutoModelForImageClassification.from_pretrained("AZIIIIIIIIZ/vit-base-patch16-224-finetuned-eurosat")
+# processor = AutoImageProcessor.from_pretrained("AZIIIIIIIIZ/vit-base-patch16-224-finetuned-eurosat")
+# model = AutoModelForImageClassification.from_pretrained("AZIIIIIIIIZ/vit-base-patch16-224-finetuned-eurosat")
+pipe = pipeline("image-classification", model="AZIIIIIIIIZ/vit-base-patch16-224-finetuned-eurosat")
 
-def predict(image):
-  return pipe(image)
 
-demo = gr.Interface(
-  fn=predict,
-  inputs='image',
-  outputs='text',
-)
+# $ pip install gradio_client fastapi uvicorn
+
+import requests
+from PIL import Image
+from transformers import pipeline
+import io
+import base64
 
-demo.launch()
+# Initialize the pipeline
+# pipe = pipeline('image-classification')
 
-# $ pip install gradio_client fastapi uvicorn
+def load_image_from_path(image_path):
+    return Image.open(image_path)
+
+def load_image_from_url(image_url):
+    response = requests.get(image_url)
+    return Image.open(io.BytesIO(response.content))
+
+def load_image_from_base64(base64_string):
+    image_data = base64.b64decode(base64_string)
+    return Image.open(io.BytesIO(image_data))
+
+def predict(image_input):
+    if isinstance(image_input, str):
+        if image_input.startswith('http'):
+            image = load_image_from_url(image_input)
+        elif image_input.startswith('/'):
+            image = load_image_from_path(image_input)
+        else:
+            image = load_image_from_base64(image_input)
+    elif isinstance(image_input, Image.Image):
+        image = image_input
+    else:
+        raise ValueError("Incorrect format used for image. Should be an URL linking to an image, a base64 string, a local path, or a PIL image.")
+    
+    return pipe(image)
+
+
+# def predict(image):
+#   return pipe(image)
+
+def main():
+    # image_input = 'path_or_url_or_base64'  # Update with actual input
+    # output = predict(image_input)
+    # print(output)
+        
+    demo = gr.Interface(
+      fn=predict,
+      inputs='image',
+      outputs='text',
+    )
+
+    demo.launch()
+
+    
 
+if __name__ == "__main__":
+    main()