Update app.py

app.py

@@ -1,22 +1,30 @@
 import gradio as gr
-from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import torch.nn.functional as F
 
-# Load sentiment analysis pipeline
-sentiment_classifier = pipeline("sentiment-analysis")
+# Load RoBERTa model
+model_name = "cardiffnlp/twitter-roberta-base-sentiment"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+labels = ["Negative", "Neutral", "Positive"]
 
 def analyze_sentiment(text):
-    result = sentiment_classifier(text)[0]
-    label = result['label']
-    score = result['score']
-    percent = f"{score * 100:.2f}%"
-    return f"{label} ({percent})"
+    inputs = tokenizer(text, return_tensors="pt")
+    with torch.no_grad():
+        logits = model(**inputs).logits
+    probs = F.softmax(logits, dim=1)[0]
+    pred = torch.argmax(probs).item()
+    confidence = probs[pred].item() * 100
+    return f"{labels[pred]} ({confidence:.2f}%)"
 
 iface = gr.Interface(
     fn=analyze_sentiment,
     inputs="text",
     outputs="text",
-    title="Simple Sentiment Analyzer",
-    description="Enter a sentence and receive its sentiment classification (Positive, Negative, or Neutral)."
+    title="RoBERTa-Based Sentiment Analyzer",
+    description="Uses CardiffNLP's sentiment model. Classifies as Positive, Neutral, or Negative with confidence."
 )
 
 iface.launch()