Create app.py

app.py

@@ -0,0 +1,49 @@
+from PIL import Image
+import matplotlib.pyplot as plt
+import gradio as gr
+from transformers import pipeline
+from transformers import AutoModelForImageClassification, AutoImageProcessor
+
+image_processor = AutoImageProcessor.from_pretrained("./Mymodel/")
+model = AutoModelForImageClassification.from_pretrained("./Mymodel/")
+
+def predict(my_image):
+
+    image = Image.fromarray(image.astype('uint8'))
+
+    pipe = pipeline("image-classification", 
+                    model=model,
+                    feature_extractor=image_processor)
+    
+    pred = pipe(image)
+    
+    plt.imshow(image)
+    plt.title(pred[0]['label'].replace('_', ' ').title())
+    plt.axis(False)
+    plt.show()
+
+    print(f"Predicted the above image as a {pred[0]['label'].replace('_', ' ').title()} with {pred[0]['score']*100:.2f}% confidence")
+    
+    run = True
+    while run:
+
+        inp = input('Is the prediction correct?')
+        if inp.lower() == 'yes':
+            print(f"""
+{food_info[pred[0]['label'].replace('_', ' ').title()]['Description']}
+
+Info: {food_info[pred[0]['label'].replace('_', ' ').title()]['Calories and Health Info']}""")
+            run = False
+        elif inp.lower() == 'no':
+            print(f"""
+The image could be a {pred[1]['label'].replace('_', ' ').title()}, with a {pred[1]['score']*100:.2f}% confidence,
+The image could be a {pred[2]['label'].replace('_', ' ').title()}, with a {pred[2]['score']*100:.2f}% confidence,
+The image could be a {pred[3]['label'].replace('_', ' ').title()}, with a {pred[3]['score']*100:.2f}% confidence,
+Or the image could be a {pred[4]['label'].replace('_', ' ').title()}, with a {pred[4]['score']*100:.2f}% confidence,
+                   """)
+            run = False
+        else:
+            print('Please respond as yes or no')
+
+iface = gr.Interface(fn=predict, inputs="image", outputs="image")
+iface.launch()