Update app.py

app.py

@@ -1,14 +1,71 @@
 import gradio as gr
-import spaces
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
+import random
 
-zero = torch.Tensor([0]).cuda()
-print(zero.device) # <-- 'cpu' 🤔
+# Load model and tokenizer
+model_name = "tabularisai/robust-sentiment-analysis"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
-@spaces.GPU
-def greet(n):
-    print(zero.device) # <-- 'cuda:0' 🤗
-    return f"Hello {zero + n} Tensor"
+# Function to predict sentiment
+def predict_sentiment(text):
+    inputs = tokenizer(text.lower(), return_tensors="pt", truncation=True, padding=True, max_length=512)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    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)
+
+# 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)
 
-demo = gr.Interface(fn=greet, inputs=gr.Number(), outputs=gr.Text())
 demo.launch()