Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load the model and tokenizer from Hugging Face Hub
+tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
+model = DistilBertForSequenceClassification.from_pretrained('jdmartinev/imdbreviews_classification_distilbert_v02')
+
+# Define the function to perform text classification
+def classify_text(input_text):
+    inputs = tokenizer(input_text, return_tensors="pt")
+    id2label = {0: "negative", 1: "positive"}
+    # Get model predictions
+    outputs = model(**inputs)
+    pred = np.argmax(outputs)# Print logits
+    return id2label[pred]
+    
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=classify_text,
+    inputs=gr.Textbox(lines=2, placeholder="Enter text to classify"),
+    outputs=gr.Tex(label="Class"),
+    title="IMDB Review Classifier",
+    description="Classify IMDB reviews using a fine-tuned DistilBERT model with LoRA.",
+)
+
+# Launch the app
+iface.launch()