Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
+
+# Load the model from the hub
+model = AutoModelForSequenceClassification.from_pretrained("MALEKSAHLIA/fine-tuned-sentiment-model-imdb")
+tokenizer = AutoTokenizer.from_pretrained("MALEKSAHLIA/fine-tuned-sentiment-model-imdb")
+
+# Create a pipeline for sentiment analysis
+nlp = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+
+def predict_sentiment(sentence):
+    result = nlp(sentence)
+    sentiment = "Positive" if result[0]['label'] == 'LABEL_1' else "Negative"  # Adjust the label to match your model's output
+    return sentiment
+
+iface = gr.Interface(
+    fn=predict_sentiment, 
+    inputs="text", 
+    outputs="text",
+    title="Sentiment Analysis",
+    description="Enter a sentence to get the sentiment (Positive or Negative)."
+)
+
+iface.launch()