Update app.py

app.py

@@ -1,35 +1,27 @@
 import streamlit as st
-from transformers import BertTokenizer, BertForSequenceClassification
-import torch
-
-# Load the fine-tuned model and tokenizer
-model_path = 'path/to/your/fine-tuned/model'
-tokenizer = BertTokenizer.from_pretrained(model_path)
-model = BertForSequenceClassification.from_pretrained(model_path)
-
-# Set the model to evaluation mode
-model.eval()
-
-# Function to perform sentiment analysis
-def analyze_sentiment(text):
-    inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True)
-    with torch.no_grad():
-        outputs = model(**inputs)
-    logits = outputs.logits
-    predicted_class = logits.argmax().item()
-    return predicted_class
-
-# Create a Streamlit app
+from transformers import pipeline
+
+model_path = "citizenlab/twitter-xlm-roberta-base-sentiment-finetunned"
+
+st.set_page_config(page_title="Sentiment Analysis App")
+
+
+sentiment_classifier = pipeline("text-classification", model=model_path, tokenizer=model_path)
+
 st.title("Sentiment Analysis App")
 
-# Get the user input
-text = st.text_area("Enter text for sentiment analysis")
+user_input = st.text_area("Enter a message:")
 
 if st.button("Analyze Sentiment"):
-    if text:
-        sentiment_class = analyze_sentiment(text)
-        sentiment_labels = ['Negative', 'Neutral', 'Positive']  # Adjust as needed
-        sentiment = sentiment_labels[sentiment_class]
-        st.write(f"Sentiment: {sentiment}")
-    else:
-        st.warning("Please enter text for analysis.")
+    if user_input:
+        # Perform sentiment analysis
+        results = sentiment_classifier(user_input)
+        sentiment_label = results[0]["label"]
+        sentiment_score = results[0]["score"]
+
+        st.write(f"Sentiment: {sentiment_label}")
+        st.write(f"Confidence Score: {sentiment_score:.2f}")
+
+# Run the Streamlit app
+if _name_ == "_main_":
+    st.write("Enter a message and click 'Analyze Sentiment' to classify its sentiment.")