Create app.py

app.py

@@ -0,0 +1,41 @@
+import streamlit as st
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+# Replace with your actual model ID or access token from Hugging Face Hub
+model_id = "your-model-id"
+
+# Load the pre-trained sentiment analysis model and tokenizer from Hugging Face Hub
+model = AutoModelForSequenceClassification.from_pretrained(model_id)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+def classify_sentiment(text):
+  """
+  Function to preprocess text, make predictions using the loaded model,
+  and return the predicted sentiment.
+  """
+  # Preprocess text (tokenization)
+  encoded_text = tokenizer(text, return_tensors="pt")
+
+  # Make prediction using the loaded model
+  output = model(**encoded_text)
+  predictions = output.logits.argmax(-1)
+
+  # Map predicted class label to sentiment category
+  sentiment_mapping = {0: "Negative", 1: "Neutral", 2: "Positive"}
+  sentiment = sentiment_mapping[predictions.item()]
+
+  return sentiment
+
+def main():
+  """
+  Streamlit app for sentiment analysis
+  """
+  st.title("Sentiment Analysis App")
+  text_input = st.text_input("Enter text to analyze:")
+
+  if text_input:
+      sentiment = classify_sentiment(text_input)
+      st.write(f"Predicted Sentiment: {sentiment}")
+
+if __name__ == "__main__":
+  main()