Update app.py

app.py

@@ -1,4 +1,54 @@
 import streamlit as st
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
+import numpy as np
+from scipy.special import softmax
+from collections import Counter
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+# Load model and tokenizer
+MODEL = "cardiffnlp/twitter-roberta-base-sentiment-latest"
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
+config = AutoConfig.from_pretrained(MODEL)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL)
+
+def analyze_sentiment_for_sentences(sentences):
+    sentiments = []
+    for sentence in sentences:
+        encoded_input = tokenizer(sentence, return_tensors='pt')
+        output = model(**encoded_input)
+        scores = output[0][0].detach().numpy()
+        scores = softmax(scores)
+        highest_sentiment_idx = np.argmax(scores)
+        highest_sentiment_label = config.id2label[highest_sentiment_idx]
+        sentiments.append(highest_sentiment_label)
+    return sentiments
+
+def calculate_sentiment_percentages(sentiments):
+    sentiment_counts = Counter(sentiments)
+    total_sentiments = len(sentiments)
+    sentiment_percentages = {"😊 positive": 0, "😐 neutral": 0, "😔 negative": 0}
+    for sentiment, count in sentiment_counts.items():
+        if sentiment == "positive":
+            sentiment_percentages["😊 positive"] = (count / total_sentiments) * 100
+        elif sentiment == "neutral":
+            sentiment_percentages["😐 neutral"] = (count / total_sentiments) * 100
+        else:
+            sentiment_percentages["😔 negative"] = (count / total_sentiments) * 100
+    return sentiment_percentages
+
+# Streamlit UI
+st.title("✨ Sentiment Analysis Web App")
+st.write("Enter sentences below to analyze their sentiment.")
+
+# User input
+user_input = st.text_area("Enter sentences (one per line):")
+if st.button("Analyze Sentiment"):
+    if user_input.strip():
+        sentences = user_input.split("\n")
+        sentences = [s.strip() for s in sentences if s.strip()]
+        sentiments = analyze_sentiment_for_sentences(sentences)
+        sentiment_percentages = calculate_sentiment_percentages(sentiments)
+        
+        st.subheader("📊 Sentiment Analysis Results")
+        st.write(sentiment_percentages)
+    else:
+        st.warning("⚠️ Please enter at least one sentence.")