Update app.py

app.py

@@ -1,16 +1,21 @@
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
-import random
 
 # Load model and tokenizer
 model_name = "tabularisai/robust-sentiment-analysis"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
+# Move model to GPU if available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
+
 # Function to predict sentiment
 def predict_sentiment(text):
     inputs = tokenizer(text.lower(), return_tensors="pt", truncation=True, padding=True, max_length=512)
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+    
     with torch.no_grad():
         outputs = model(**inputs)
     
@@ -18,54 +23,31 @@ def predict_sentiment(text):
     predicted_class = torch.argmax(probabilities, dim=-1).item()
     
     sentiment_map = {0: "Very Negative", 1: "Negative", 2: "Neutral", 3: "Positive", 4: "Very Positive"}
-    return sentiment_map[predicted_class], {k: float(v) for k, v in zip(sentiment_map.values(), probabilities[0])}
-
-# Function to generate random example
-def random_example():
-    examples = [
-        "I absolutely loved this movie! The acting was superb and the plot was engaging.",
-        "The service at this restaurant was terrible. I'll never go back.",
-        "The product works as expected. Nothing special, but it gets the job done.",
-        "I'm somewhat disappointed with my purchase. It's not as good as I hoped.",
-        "This book changed my life! I couldn't put it down and learned so much."
-    ]
-    return random.choice(examples)
+    confidence = probabilities[0][predicted_class].item()
+    
+    return sentiment_map[predicted_class], f"{confidence:.2%}"
 
 # Gradio interface
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown(
-    """
-    # 🎭 Sentiment Analysis Wizard
-    Discover the emotional tone behind any text with our advanced AI model!
-    """
-    )
-    
-    with gr.Row():
-        with gr.Column(scale=2):
-            text_input = gr.Textbox(label="Enter your text here", placeholder="Type or paste your text...")
-            random_btn = gr.Button("Get Random Example")
-        
-        with gr.Column(scale=1):
-            sentiment_output = gr.Textbox(label="Overall Sentiment")
-            confidence_output = gr.Label(label="Confidence Scores")
-    
-    analyze_btn = gr.Button("Analyze Sentiment", variant="primary")
-    
-    gr.Markdown(
-    """
-    ### How it works
-    This app uses a state-of-the-art language model to analyze the sentiment of your text.
-    It classifies the input into one of five categories: Very Negative, Negative, Neutral, Positive, or Very Positive.
-    
-    Try it out with your own text or click "Get Random Example" for inspiration!
-    """
-    )
-    
-    def analyze(text):
-        sentiment, confidences = predict_sentiment(text)
-        return sentiment, confidences
-    
-    analyze_btn.click(analyze, inputs=text_input, outputs=[sentiment_output, confidence_output])
-    random_btn.click(random_example, outputs=text_input)
+def gradio_sentiment_analysis(text):
+    sentiment, confidence = predict_sentiment(text)
+    return f"Sentiment: {sentiment}\nConfidence: {confidence}"
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=gradio_sentiment_analysis,
+    inputs=gr.Textbox(lines=5, label="Enter text for sentiment analysis"),
+    outputs=gr.Textbox(label="Result"),
+    title="Sentiment Analysis",
+    description="Analyze the sentiment of your text using a 5-class sentiment model.",
+    theme="huggingface",
+    examples=[
+        ["I absolutely loved this movie! The acting was superb and the plot was engaging."],
+        ["The service at this restaurant was terrible. I'll never go back."],
+        ["The product works as expected. Nothing special, but it gets the job done."],
+        ["I'm somewhat disappointed with my purchase. It's not as good as I hoped."],
+        ["This book changed my life! I couldn't put it down and learned so much."]
+    ]
+)
 
-demo.launch()
+# Launch the app
+iface.launch()