Create app.py

app.py

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+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import gradio as gr
+
+# Load model and tokenizer
+model_name = "output/checkpoint-2500/"
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Define prediction function
+def predict_sentiment(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+    outputs = model(**inputs)
+    logits = outputs.logits
+    probabilities = logits.softmax(dim=1)
+    sentiment = "Positive" if probabilities[0][1] > 0.5 else "Negative"
+    return sentiment
+
+# Create Gradio interface
+text_input = gr.Textbox(lines=7, label="Input Text", placeholder="Enter your text here...")
+output_text = gr.Textbox(label="Predicted Sentiment")
+
+# Author information
+author = "Ajeetkumar Ukande"
+
+# Create Gradio interface
+interface = gr.Interface(predict_sentiment, text_input, output_text, 
+             title="<div style='color: #336699; font-size: 24px; font-weight: bold; border: 2px solid #336699; padding: 10px; border-radius: 10px;'>Sentiment-Analysis-FineTuned-DistilBERT</div>", 
+             description=f"""<div style='color: #666666; font-family: Arial, sans-serif;'>
+                             <p style='margin-top: 10px;'>This model predicts the sentiment of text.</p>
+                             <p>It uses a fine-tuned DistilBERT model trained on IMDb movie reviews dataset.</p>
+                             <p>The sentiment is classified as Positive if the probability of positive sentiment is greater than 0.5, otherwise it's classified as Negative.</p>
+                             <p>Developed by <span style='color: #336699; font-weight: bold;'>{author}</span>.</p>
+                             </div>""", 
+             theme="huggingface",
+             allow_flagging=False,
+             )
+
+interface.launch(share=True)