add app file

app.py

@@ -0,0 +1,47 @@
+import streamlit as st
+from transformers import BertForSequenceClassification, BertTokenizer
+from sklearn.preprocessing import LabelEncoder
+import torch
+import numpy as np
+
+# Load model and label encoder
+@st.cache(allow_output_mutation=True)
+def load_model_and_label_encoder():
+    fine_tuned_model = BertForSequenceClassification.from_pretrained('./fine_tuned_model')
+    label_encoder = LabelEncoder()
+    label_encoder.classes_ = np.load('./label_encoder_classes.npy', allow_pickle=True)
+    tokenizer = BertTokenizer.from_pretrained("./tokenizer")  # Load tokenizer from local file
+    return fine_tuned_model, label_encoder, tokenizer
+
+def predict(symptom, fine_tuned_model, label_encoder, tokenizer):
+    user_input_encoding = tokenizer(symptom, padding=True, truncation=True, return_tensors='pt', max_length=512, return_attention_mask=True, return_token_type_ids=True)
+
+    with torch.no_grad():
+        logits = fine_tuned_model(**user_input_encoding)
+        probabilities = torch.nn.functional.softmax(logits.logits, dim=1).numpy()[0]
+        predicted_labels = np.argsort(-probabilities)[:5]
+        predicted_diseases = label_encoder.inverse_transform(predicted_labels)
+        predicted_probabilities = probabilities[predicted_labels]
+
+    predictions = [{'disease': disease, 'probability': probability * 100} for disease, probability in zip(predicted_diseases, predicted_probabilities)]
+    return predictions
+
+def main():
+    st.title("Disease Prediction App")
+
+    # Load model and label encoder
+    fine_tuned_model, label_encoder, tokenizer = load_model_and_label_encoder()
+
+    # Input
+    symptom = st.text_input("Enter symptom:", "")
+
+    # Predict
+    if st.button("Predict"):
+        if symptom:
+            predictions = predict(symptom, fine_tuned_model, label_encoder, tokenizer)
+            st.write("Top 5 Predictions:")
+            for prediction in predictions:
+                st.write(f"Disease: {prediction['disease']}, Probability: {prediction['probability']:.2f}%")
+
+if __name__ == '__main__':
+    main()