Simplified model and dataset loading for testing

app.py

@@ -1 +1,26 @@
-print("Hello, World!")
+import gradio as gr
+from datasets import load_dataset
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Check if GPU is available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load the IMDb dataset
+dataset = load_dataset('imdb', split='test[:1%]')  # Load a small portion for testing
+
+# Initialize the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased')
+model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=2)
+model.to(device)
+
+# Function to classify sentiment
+def classify_text(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(device)
+    outputs = model(**inputs)
+    prediction = torch.argmax(outputs.logits, dim=-1).item()
+    return "Positive" if prediction == 1 else "Negative"
+
+# Set up the Gradio interface
+iface = gr.Interface(fn=classify_text, inputs="text", outputs="text")
+iface.launch()