Update app.py

app.py

@@ -1,30 +1,72 @@
 import gradio as gr
-from transformers import pipeline
+from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
+import torch
 
-# Initialize the sentiment analysis pipeline with your model
-sentiment_pipeline = pipeline("sentiment-analysis", model="quocviethere/imdb-roberta")
+# -----------------------------
+# Configuration Section
+# -----------------------------
+MODEL_NAME = "quocviethere/imdb-roberta"  # Replace with your actual model ID
 
+# -----------------------------
+# Model Loading Section
+# -----------------------------
+try:
+    # Load tokenizer and model from Hugging Face Hub
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+    model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
+    
+    # Initialize the sentiment analysis pipeline
+    sentiment_pipeline = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+    
+    # Verify label mapping
+    label_mapping = model.config.id2label
+    print(f"Model label mapping: {label_mapping}")
+except Exception as e:
+    print(f"Error loading model: {e}")
+    raise
+
+# -----------------------------
+# Sentiment Analysis Function
+# -----------------------------
 def analyze_sentiment(text):
-    result = sentiment_pipeline(text)[0]
-    label = result['label']
-    score = result['score']
-    sentiment = "Positive 😊" if label == "POSITIVE" else "Negative 😞"
-    confidence = f"Confidence: {round(score * 100, 2)}%"
-    return sentiment, confidence
+    try:
+        # Perform sentiment analysis
+        result = sentiment_pipeline(text)[0]
+        
+        # Extract label and score
+        label = result['label']
+        score = result['score']
+        
+        # Map label to sentiment
+        if label in label_mapping.values():
+            sentiment = "Positive 😊" if label == "POSITIVE" else "Negative 😞"
+        else:
+            # Handle unexpected labels
+            sentiment = label
+            print(f"Unexpected label received: {label}")
+        
+        confidence = f"Confidence: {round(score * 100, 2)}%"
+        
+        return sentiment, confidence
+    except Exception as e:
+        print(f"Error during sentiment analysis: {e}")
+        return "Error", "Could not process the input."
 
-# Define the Gradio interface using the updated API
+# -----------------------------
+# Gradio Interface Section
+# -----------------------------
 iface = gr.Interface(
     fn=analyze_sentiment,
     inputs=gr.Textbox(
         lines=5,
-        placeholder="Enter a review here...",
-        label="Review"
+        placeholder="Enter a movie review here...",
+        label="Movie Review"
     ),
     outputs=[
         gr.Textbox(label="Sentiment"),
         gr.Textbox(label="Confidence")
     ],
-    title="Sentiment Analysis",
+    title="IMDb Sentiment Analysis with RoBERTa",
     description="Analyze the sentiment of movie reviews using a fine-tuned RoBERTa model.",
     examples=[
         ["I loved the cinematography and the story was captivating."],
@@ -33,5 +75,7 @@ iface = gr.Interface(
     theme="default"
 )
 
-# Launch the interface
-iface.launch()
+# -----------------------------
+# Launch the Interface
+# -----------------------------
+iface.launch()