Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import torch
+from transformers import RobertaTokenizer, RobertaForSequenceClassification
+
+# Load model
+model_name = "syedkhalid076/RoBERTa-Sentimental-Analysis-Model"
+tokenizer = RobertaTokenizer.from_pretrained(model_name)
+model = RobertaForSequenceClassification.from_pretrained(model_name)
+model.eval()
+
+# 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"
+    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)
+    return df
+
+# Streamlit app
+def main():
+    st.title("Sentiment Analysis App")
+    st.write("Write a sentence or upload a CSV file to analyze sentiment.")
+
+    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)
+
+    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 __name__ == '__main__':
+    main()