Update app.py

app.py

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