Update app.py

app.py

@@ -7,9 +7,14 @@ from tensorflow.keras.models import load_model
 from official.nlp.data import classifier_data_lib
 from official.nlp.tools import tokenization
 import joblib
+import zipfile
+import gradio as gr
 
 model = load_model('best_model.h5', custom_objects={'KerasLayer': hub.KerasLayer})
 
+with zipfile.ZipFile('model.zip', 'r') as zip_ref:
+    zip_ref.extractall('model')
+
 
 vocab_file = model.resolved_object.vocab_file.asset_path.numpy()
 do_lower_case = model.resolved_object.do_lower_case.numpy()
@@ -53,4 +58,37 @@ def preprocess_new_data(texts):
     dataset = dataset.batch(32, drop_remainder=False)
     dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)
     
-    return dataset
+    return dataset
+def launch(input):
+    # Load the label encoder
+    label_encoder = joblib.load('label_encoder.joblib')
+
+    # Preprocess the new data
+    sample_example = [input]
+    new_data_dataset = preprocess_new_data(sample_example)
+
+    # Assuming you have a model already loaded (add model loading code if needed)
+    # Make predictions on the new data
+    predictions = model.predict(new_data_dataset)
+
+    # Decode the predictions
+    predicted_classes = [label_list[np.argmax(pred)] for pred in predictions]
+
+    # Print the predicted classes
+    print(predicted_classes)
+
+    # Calculate the highest probabilities
+    highest_probabilities = [max(instance) for instance in predictions]
+
+    # Decode labels using the label encoder
+    decoded_labels = label_encoder.inverse_transform(predicted_classes)
+
+    print("Most likely ISCO code is {} and probability is {}".format(decoded_labels,highest_probabilities))
+
+# Gradio Interface
+iface = gr.Interface(fn=launch,
+                     inputs=gr.inputs.Textbox(lines=2, placeholder="Enter job title and description here..."),
+                     outputs="text")
+
+# Launch the Gradio app
+iface.launch()