app

app.py

@@ -1,28 +1,6 @@
 import gradio as gr
-from fastai.vision.all import *
-import skimage
-import timm
-learn = load_learner('/content/drive/MyDrive/Rice Category Prediction/export.pkl')
+def greet(name):
+    return "Hello " + name + "!!"
 
-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))}
-
-title = "Rice Category 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 = ['/content/drive/MyDrive/Rice Category Prediction/Arborio.jpg',
-              '/content/drive/MyDrive/Rice Category Prediction/Basmati.jpg',
-              '/content/drive/MyDrive/Rice Category Prediction/Jasmine.jpg',
-              '/content/drive/MyDrive/Rice Category Prediction/Ispala.jpg',
-              '/content/drive/MyDrive/Rice Category Prediction/Karacadag.jpg'
-
-          ]
-interpretation='default'
-enable_queue=True
-
-demo = gr.Interface(fn=predict, inputs="image", outputs="label",examples= examples)
-if __name__ == "__main__":
-    demo.launch()
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()