aopp

app.py

@@ -9,32 +9,25 @@ from blurr.text.modeling.all import *
 model_path = "origin-classifier-stage-2.pkl"
 dls_path = "dls_origin-classifier_v1.pkl"
 
-# Load the learner
 learner_inf = load_learner(model_path)
 
-# Load the DataLoaders
 dls = torch.load(dls_path)
 
-# Create a mapping from class labels to indices
 class_label_mapping = {label: idx for idx, label in enumerate(learner_inf.dls.vocab)}
 
-# Define a function to make predictions
 def predict_text(text):
-    prediction = learner_inf.blurr_predict(text)[0]
-    predicted_probs = prediction['scores']
-    predicted_labels = prediction['class_labels']
+    prediction = learner_inf.blurr_predict(text)
+    predicted_probs = prediction[0]['scores']
+    predicted_labels = prediction[0]['class_labels']
     result = {label: f"{prob*100:.2f}%" for label, prob in zip(predicted_labels, predicted_probs)}
     return result
 
-# Create a Gradio interface
 iface = gr.Interface(
     fn=predict_text,
-    inputs="text",
-    outputs="label",
+    inputs=gr.inputs.Textbox(lines=2, placeholder='Enter Recipe Here...'),
+    outputs=gr.outputs.Label(num_top_classes=3),
     title="Food Origin Classification App",
     description="Enter a Recipe, and it will predict the class label.",
 )
 
-# Start the Gradio app
-iface.launch()
-
+iface.launch()