Update app.py

app.py

@@ -1,35 +1,34 @@
-import streamlit as st
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-import torch
-
-# Specify the local paths where the model and tokenizer are saved
-model_dir = "path_to_save_model"
-tokenizer_dir = "path_to_save_tokenizer"
-
-# Load the model and tokenizer from the local directories
-model = AutoModelForSequenceClassification.from_pretrained(model_dir)
-tokenizer = AutoTokenizer.from_pretrained(tokenizer_dir)
-
-# Function to predict the sentiment
-def predict_sentiment(text):
-    inputs = tokenizer(text, return_tensors="pt")
-    with torch.no_grad():
-        outputs = model(**inputs)
-    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
-    return torch.argmax(probs, dim=-1).item(), probs
-
-# Streamlit interface
-st.title("KIWI Classifier")
-st.write("Enter a question or statement to classify:")
-
-user_input = st.text_area("Your input", "")
-if st.button("Classify"):
-    if user_input:
-        label, probabilities = predict_sentiment(user_input)
-        st.write(f"Prediction: {label}")
-        st.write(f"Probabilities: {probabilities.tolist()}")
-    else:
-        st.write("Please enter some text to classify.")
-
-# Additional instructions or information
-st.write("This application uses a fine-tuned BERT model to classify questions and statements.")
+import streamlit as st
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+# Specify the Hugging Face model repository
+model_name = "DanKoan/kiwi-classifier"
+
+# Load the model and tokenizer from the Hugging Face Hub
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Function to predict the sentiment
+def predict_sentiment(text):
+    inputs = tokenizer(text, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    return torch.argmax(probs, dim=-1).item(), probs
+
+# Streamlit interface
+st.title("KIWI Classifier")
+st.write("Enter a question or statement to classify:")
+
+user_input = st.text_area("Your input", "")
+if st.button("Classify"):
+    if user_input:
+        label, probabilities = predict_sentiment(user_input)
+        st.write(f"Prediction: {label}")
+        st.write(f"Probabilities: {probabilities.tolist()}")
+    else:
+        st.write("Please enter some text to classify.")
+
+# Additional instructions or information
+st.write("This application uses a fine-tuned BERT model to classify questions and statements.")