Update app.py

app.py

@@ -9,39 +9,50 @@ tokenizer = RobertaTokenizer.from_pretrained(model_name)
 model = RobertaForSequenceClassification.from_pretrained(model_name)
 model.eval()
 
+# Define sentiment labels
+sentiment_labels = {0: "Negative", 1: "Neutral", 2: "Positive"}
+
 # Function to predict sentiment for a single sentence
 def predict_sentiment(sentence):
     inputs = tokenizer(sentence, return_tensors="pt", max_length=512, truncation=True)
     outputs = model(**inputs)
-    logits = outputs.logits.detach().cpu().numpy()
-    sentiment = "positive" if logits[0][1] > logits[0][0] else "negative"
+    logits = outputs.logits.detach().cpu()
+    predicted_class = torch.argmax(logits, dim=-1).item()
+    sentiment = sentiment_labels[predicted_class]
     return sentiment
 
 # Function to process CSV file and predict sentiment for each row
 def process_csv(file):
     df = pd.read_csv(file)
-    df['Sentiment'] = df['Text'].apply(predict_sentiment)
+    if 'Text' not in df.columns:
+        st.error("CSV file must have a 'Text' column with sentences for analysis.")
+        return None
+    df['sentiment'] = df['text'].apply(predict_sentiment)
     return df
 
 # Streamlit app
 def main():
     st.title("Sentiment Analysis App")
-    st.write("Write a sentence or upload a CSV file to analyze sentiment.")
-    st.write("NOTE: If uploading a CSV file, please rename your column's name with the text/sentence to 'Text', where 'T' is in upper-case and the rest in lower-case")
+    st.write("Analyze text sentiment as Negative, Neutral, or Positive.")
+    st.write("NOTE: If uploading a CSV file, ensure the column containing text is named 'text' (case-sensitive).")
 
     option = st.radio("Choose input type:", ("Write a sentence", "Upload a CSV file"))
 
     if option == "Write a sentence":
         sentence = st.text_input("Enter a sentence:")
         if st.button("Analyze"):
-            sentiment = predict_sentiment(sentence)
-            st.write("Sentiment:", sentiment)
+            if sentence.strip():
+                sentiment = predict_sentiment(sentence)
+                st.write("Sentiment:", sentiment)
+            else:
+                st.warning("Please enter a valid sentence.")
 
     elif option == "Upload a CSV file":
         file = st.file_uploader("Upload CSV file", type=['csv'])
         if file is not None:
             df = process_csv(file)
-            st.write(df)
+            if df is not None:
+                st.write(df)
 
 if __name__ == '__main__':
     main()