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app.py

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+import gradio as gr
+import torch
+from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
+
+# Load the model and tokenizer
+model_name = "AventIQ-AI/distilbert-base-uncased-sentiment-analysis"
+tokenizer = DistilBertTokenizer.from_pretrained(model_name)
+model = DistilBertForSequenceClassification.from_pretrained(model_name)
+
+def predict_sentiment(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
+    with torch.no_grad():
+        logits = model(**inputs).logits
+    predicted_class_id = torch.argmax(logits, dim=-1).item()
+    sentiment = "Positive" if predicted_class_id == 1 else "Negative"
+    return sentiment
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.Textbox(lines=3, placeholder="Enter text for sentiment analysis..."),
+    outputs=gr.Textbox(label="Sentiment"),
+    title="DistilBERT Sentiment Analysis",
+    description="Enter a sentence to classify its sentiment as Positive or Negative using a fine-tuned DistilBERT model.",
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+torch
+transformers
+sentencepiece
+gradio