Update app.py

app.py

@@ -1,71 +1,71 @@
-#import gradio as gr
-#import tensorflow as tf
-#from transformers import TFDistilBertForSequenceClassification, DistilBertTokenizer
-#
-## Load model and tokenizer
-#model_save_path = "saved_model"  # replace with the actual path
-#model = TFDistilBertForSequenceClassification.from_pretrained(model_save_path)
-#tokenizer = DistilBertTokenizer.from_pretrained(model_save_path)
-#
-#def predict(question1, question2):
-#    inputs = tokenizer(
-#        [question1], [question2],
-#        return_tensors='tf',
-#        truncation=True,
-#        padding=True,
-#        max_length=50
-#    )
-#    outputs = model(inputs)
-#    logits = outputs.logits
-#    probabilities = tf.nn.softmax(logits, axis=-1)
-#    prediction = tf.argmax(probabilities, axis=1).numpy()[0]
-#    prob = probabilities.numpy()[0]
-#    return f"{'Duplicate' if prediction == 1 else 'Not Duplicate'} (Probability: {prob})"
-#
-## Gradio interface
-#interface = gr.Interface(
-#    fn=predict,
-#    inputs=["text", "text"],
-#    outputs="text",
-#    title="Duplicate Question Detection",
-#    description="Enter two questions to check if they are duplicates."
-#)
-#
-#interface.launch()
-
-import streamlit as st
-import tensorflow as tf
-from transformers import TFDistilBertForSequenceClassification, DistilBertTokenizer
-
-# Load model and tokenizer
-model_save_path = "./saved_model"  # replace with the actual path
-model = TFDistilBertForSequenceClassification.from_pretrained(model_save_path)
-tokenizer = DistilBertTokenizer.from_pretrained(model_save_path)
-
-# Streamlit app
-st.title("Duplicate Question Detection")
-
-question1 = st.text_input("Enter the first question:")
-question2 = st.text_input("Enter the second question:")
-
-if st.button("Predict"):
-    if question1 and question2:
-        inputs = tokenizer(
-            [question1], [question2],
-            return_tensors='tf',
-            truncation=True,
-            padding=True,
-            max_length=50
-        )
-        outputs = model(inputs)
-        logits = outputs.logits
-        probabilities = tf.nn.softmax(logits, axis=-1)
-        prediction = tf.argmax(probabilities, axis=1).numpy()[0]  # 0 or 1
-        prob = probabilities.numpy()[0]
-
-        st.write(f"Prediction: {'Duplicate' if prediction == 1 else 'Not Duplicate'}")
-        st.write(f"Probability: {prob}")
-    else:
-        st.write("Please enter both questions.")
-
-
+#import gradio as gr
+#import tensorflow as tf
+#from transformers import TFDistilBertForSequenceClassification, DistilBertTokenizer
+#
+## Load model and tokenizer
+#model_save_path = "saved_model"  # replace with the actual path
+#model = TFDistilBertForSequenceClassification.from_pretrained(model_save_path)
+#tokenizer = DistilBertTokenizer.from_pretrained(model_save_path)
+#
+#def predict(question1, question2):
+#    inputs = tokenizer(
+#        [question1], [question2],
+#        return_tensors='tf',
+#        truncation=True,
+#        padding=True,
+#        max_length=50
+#    )
+#    outputs = model(inputs)
+#    logits = outputs.logits
+#    probabilities = tf.nn.softmax(logits, axis=-1)
+#    prediction = tf.argmax(probabilities, axis=1).numpy()[0]
+#    prob = probabilities.numpy()[0]
+#    return f"{'Duplicate' if prediction == 1 else 'Not Duplicate'} (Probability: {prob})"
+#
+## Gradio interface
+#interface = gr.Interface(
+#    fn=predict,
+#    inputs=["text", "text"],
+#    outputs="text",
+#    title="Duplicate Question Detection",
+#    description="Enter two questions to check if they are duplicates."
+#)
+#
+#interface.launch()
+
+import streamlit as st
+import tensorflow as tf
+from transformers import TFDistilBertForSequenceClassification, DistilBertTokenizer
+
+# Load model and tokenizer
+model_save_path = "./saved_model"  # replace with the actual path
+model = TFDistilBertForSequenceClassification.from_pretrained(model_save_path)
+tokenizer = DistilBertTokenizer.from_pretrained(model_save_path)
+
+# Streamlit app
+st.title("Duplicate Question Detection")
+
+question1 = st.text_input("Enter the first question:")
+question2 = st.text_input("Enter the second question:")
+
+if st.button("Predict"):
+    if question1 and question2:
+        inputs = tokenizer(
+            [question1], [question2],
+            return_tensors='tf',
+            truncation=True,
+            padding=True,
+            max_length=50
+        )
+        outputs = model(inputs)
+        logits = outputs.logits
+        probabilities = tf.nn.softmax(logits, axis=-1)
+        prediction = tf.argmax(probabilities, axis=1).numpy()[0]  # 0 or 1
+        prob = probabilities.numpy()[0]
+
+        st.success(f"Prediction: {'Duplicate' if prediction == 1 else 'Not Duplicate'}")
+        st.success(f"Probability: Not Duplicate {prob[0]}    Duplicate {prob[1]}")
+    else:
+        st.write("Please enter both questions.")
+
+