Update app.py

app.py

@@ -1,31 +1,18 @@
-import gradio as gr
-import tensorflow as tf
+from transformers import AutoFeatureExtractor, AutoModelForImageClassification
 
+extractor = AutoFeatureExtractor.from_pretrained("swww/test")
 
-title = "Covid 19 Prediction App using X-ray Images"
+model = AutoModelForImageClassification.from_pretrained("swww/test")
 
-head = (
-  "<center>"
-  "Upload an X-ray image to check for covid19. The app is for research purposes and not clinically authorized"
-  "</center>"
-)
-title = "Covid 19 Prediction App using X-ray Images"
+import requests
 
-head = (
-  "<center>"
-  "Upload an X-ray image to check for covid19. The app is for research purposes and not clinically authorized"
-  "</center>"
-)
+API_URL = "https://api-inference.huggingface.co/models/swww/test"
+headers = {"Authorization": "Bearer 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"}
 
-def predict_input_image(img):
-   from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+def query(filename):
+    with open(filename, "rb") as f:
+        data = f.read()
+    response = requests.post(API_URL, headers=headers, data=data)
+    return response.json()
 
-   extractor = AutoFeatureExtractor.from_pretrained("swww/test")
-
-   model = AutoModelForImageClassification.from_pretrained("swww/test")
-
-
-image = gr.inputs.Image(shape=(500, 500), image_mode='L', invert_colors=False, source="upload")
-label = gr.outputs.Label()
-iface = gr.Interface(fn=predict_input_image, inputs=image, outputs=label,title=title, description=head)
-iface.launch()
+output = query("cats.jpg")