Add initial implementation of sentiment analysis model with Gradio UI

.gitignore

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+.env
+.gradio

.gradio/certificate.pem

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__init__.py

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+from transformers import BertTokenizer, BertForSequenceClassification
+import torch
+import gradio as gr
+
+# Load saved model and tokenizer
+model = BertForSequenceClassification.from_pretrained("./imdb_bert_model")
+tokenizer = BertTokenizer.from_pretrained("./imdb_bert_model")
+
+# Prediction function
+def predict_sentiment(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    outputs = model(**inputs)
+    logits = outputs.logits
+    prediction = torch.argmax(logits, dim=1).item()
+    sentiment = "Positive 😊" if prediction == 1 else "Negative 😠"
+    return sentiment
+
+# Responsive UI with gr.Blocks
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("## 🎬 IMDB Movie Review Sentiment Analyzer")
+    gr.Markdown("Write a movie review below and let BERT tell you if it's **Positive** or **Negative** 🎯")
+
+    with gr.Row():
+        with gr.Column(scale=2):
+            review_input = gr.Textbox(
+                label="Enter Review",
+                placeholder="e.g. This movie had me on the edge of my seat!",
+                lines=5,
+                max_lines=8,
+                autofocus=True
+            )
+            submit_btn = gr.Button("🔍 Analyze")
+        with gr.Column(scale=1):
+            result_output = gr.Label(label="Predicted Sentiment")
+
+    gr.Examples(
+        examples=[
+            ["This movie was absolutely amazing and so emotional!"],
+            ["Worst film I’ve ever seen. Total waste of time."],
+            ["The story was okay, but the acting saved it."],
+            ["A beautiful piece of storytelling. I loved it!"],
+        ],
+        inputs=[review_input]
+    )
+
+    submit_btn.click(fn=predict_sentiment, inputs=review_input, outputs=result_output)
+
+# Launch
+demo.launch(share=True)

requirements.txt

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+huggingface_hub
+transformers 
+torch
+gradio