Add Gradio interface to main.py

main.py

@@ -1,8 +1,10 @@
+import gradio as gr
+from datasets import load_dataset
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
 import torch
-print(torch.cuda.is_available())
 
-from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments
-from datasets import load_dataset
+# Check if GPU is available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 # Load the IMDb dataset
 dataset = load_dataset('imdb')
@@ -10,6 +12,7 @@ dataset = load_dataset('imdb')
 # 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)
 
 # Tokenize the dataset
 def tokenize_function(examples):
@@ -39,6 +42,13 @@ trainer = Trainer(
 # Train the model
 trainer.train()
 
-# Evaluate the model
-results = trainer.evaluate()
-print(results)
+# 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()