Update app.py

app.py

@@ -1,43 +1,20 @@
 import gradio as gr
-import torch
-from huggingface_hub import from_pretrained_fastai
-from pathlib import Path
-import gradio.components as components
-from gradio.blocks import Block
-from gradio import encryptor, external, networking, queueing, routes, strings, utils
-from gradio import components, utils
+from fastai.vision.all import *
+import skimage
 
+learn = load_learner('resnett.pkl')
 
-examples = ["akiec.jpg", 
-            "mel.jpg",]
-    # ⚠️ Type of model/library unknown.
-  
-   
-repo_id = "Saim8250/Skin-Diseases-Classification"
-path = Path("./")
+labels = learn.dls.vocab
+def predict(img):
+    img = PILImage.create(img)
+    pred,pred_idx,probs = learn.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
 
-def get_y(r):
-    return r["label"]
-    
-def get_x(r):
-    return path/r["fname"]
-    
-learner = from_pretrained_fastai(repo_id)
-labels = learner.dls.vocab
+title = "Skin Diseases Classifier"
+#description = "A pet breed classifier trained on the Oxford Pets dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces."
+#article="<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
+#examples = ['siamese.jpg']
+interpretation='default'
+enable_queue=True
 
-def inference(image):
-    label_predict,_,probs = learner.predict(image)
-    labels_probs = {labels[i]: float(probs[i]) for i, _ in enumerate(labels)}
-    return labels_probs
-    
-
-
-gr.Interface(
-    fn=inference,
-    title="Skin Diseases classification",
-    description = "Predict which type of skin disease",
-    inputs="image",
-    examples=examples,
-    outputs=gr.outputs.Label(num_top_classes=5, label='Prediction'),
-    cache_examples=False,
-).launch(debug=True, enable_queue=False)
+gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,article=article,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()