Update app.py

app.py

@@ -1,35 +1,35 @@
-import streamlit as st
-from transformers import DistilBertTokenizer, TFDistilBertForSequenceClassification
-import tensorflow as tf
-
-# Load the pre-trained model and tokenizer
-model_path = 'drive-download-20241117T174204Z-001/'
-loaded_model = TFDistilBertForSequenceClassification.from_pretrained(model_path)
-loaded_tokenizer = DistilBertTokenizer.from_pretrained(model_path)
-
-# Define the prediction function
-def predict_with_loaded_model(in_sentences):
-    labels = ["non-stress", "stress"]
-    inputs = loaded_tokenizer(in_sentences, return_tensors="tf", padding=True, truncation=True, max_length=512)
-    predictions = loaded_model(inputs)
-    predicted_labels = tf.argmax(predictions.logits, axis=-1).numpy()
-    predicted_probs = tf.nn.softmax(predictions.logits, axis=-1).numpy()
-
-    return [{"text": sentence, "confidence": probs.tolist(), "label": labels[label]} for sentence, label, probs in zip(in_sentences, predicted_labels, predicted_probs)]
-
-# Streamlit interface
-st.title("Stress Prediction with DistilBERT")
-
-# Add a text input box for the user to enter a sentence
-user_input = st.text_area("Enter a sentence or text:", "")
-
-# When the user clicks "Predict", run the prediction function
-if st.button("Predict"):
-    if user_input:
-        # Make the prediction using the model
-        prediction = predict_with_loaded_model([user_input])[0]
-        st.write(f"Text: {prediction['text']}")
-        st.write(f"Prediction: {prediction['label']}")
-        st.write(f"Confidence: {prediction['confidence']}")
-    else:
-        st.write("Please enter a sentence to predict.")
+import streamlit as st
+from transformers import DistilBertTokenizer, TFDistilBertForSequenceClassification
+import tensorflow as tf
+
+# Load the pre-trained model and tokenizer
+model_path = 'shukdevdatta123/Stress_Prediction_DistillBert/'
+loaded_model = TFDistilBertForSequenceClassification.from_pretrained(model_path)
+loaded_tokenizer = DistilBertTokenizer.from_pretrained(model_path)
+
+# Define the prediction function
+def predict_with_loaded_model(in_sentences):
+    labels = ["non-stress", "stress"]
+    inputs = loaded_tokenizer(in_sentences, return_tensors="tf", padding=True, truncation=True, max_length=512)
+    predictions = loaded_model(inputs)
+    predicted_labels = tf.argmax(predictions.logits, axis=-1).numpy()
+    predicted_probs = tf.nn.softmax(predictions.logits, axis=-1).numpy()
+
+    return [{"text": sentence, "confidence": probs.tolist(), "label": labels[label]} for sentence, label, probs in zip(in_sentences, predicted_labels, predicted_probs)]
+
+# Streamlit interface
+st.title("Stress Prediction with DistilBERT")
+
+# Add a text input box for the user to enter a sentence
+user_input = st.text_area("Enter a sentence or text:", "")
+
+# When the user clicks "Predict", run the prediction function
+if st.button("Predict"):
+    if user_input:
+        # Make the prediction using the model
+        prediction = predict_with_loaded_model([user_input])[0]
+        st.write(f"Text: {prediction['text']}")
+        st.write(f"Prediction: {prediction['label']}")
+        st.write(f"Confidence: {prediction['confidence']}")
+    else:
+        st.write("Please enter a sentence to predict.")