Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification
+from datetime import datetime
+import csv
+import os
+
+# Load model and tokenizer
+model = DistilBertForSequenceClassification.from_pretrained("debojit01/course-review-sentiment")
+tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert-base-uncased")
+
+labels = ['negative', 'neutral', 'positive']
+
+# Setup log files
+log_path = "logs.csv"
+corrections_path = "corrections.csv"
+
+for path, headers in [(log_path, ["timestamp", "input_text", "predicted_label"]),
+                      (corrections_path, ["timestamp", "input_text", "predicted_label", "user_correction"])]:
+    if not os.path.exists(path):
+        with open(path, mode='w', newline='') as file:
+            writer = csv.writer(file)
+            writer.writerow(headers)
+
+def classify_review(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    label_idx = torch.argmax(probs).item()
+    predicted_label = labels[label_idx]
+
+    # Logging
+    with open(log_path, mode='a', newline='') as file:
+        writer = csv.writer(file)
+        writer.writerow([datetime.now().isoformat(), text, predicted_label])
+    
+    return {label: float(prob) for label, prob in zip(labels, probs[0])}, text, predicted_label
+
+def save_correction(text, predicted_label, user_correction):
+    if user_correction != predicted_label:
+        with open(corrections_path, mode='a', newline='') as file:
+            writer = csv.writer(file)
+            writer.writerow([datetime.now().isoformat(), text, predicted_label, user_correction])
+    return f"Thanks! Correction recorded: {user_correction}"
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# 📘 Course Review Sentiment Classifier")
+    gr.Markdown("Enter a course review and get the sentiment prediction. You can correct the result if needed.")
+
+    input_text = gr.Textbox(lines=4, placeholder="Enter course review here...")
+    output_label = gr.Label(num_top_classes=3)
+    predict_btn = gr.Button("Classify")
+
+    with gr.Row(visible=False) as correction_row:
+        gr.Markdown("### ❓ Is the prediction wrong?")
+        correction_dropdown = gr.Dropdown(choices=labels, label="Correct Sentiment")
+        submit_btn = gr.Button("Submit Correction")
+        correction_status = gr.Textbox(interactive=False)
+
+    hidden_text = gr.Textbox(visible=False)
+    hidden_pred = gr.Textbox(visible=False)
+
+    def show_correction_ui(result, text, pred):
+        return result, gr.update(visible=True), text, pred
+
+    predict_btn.click(classify_review, inputs=input_text, outputs=[output_label, hidden_text, hidden_pred])\
+               .then(show_correction_ui, outputs=[output_label, correction_row, hidden_text, hidden_pred])
+
+    submit_btn.click(save_correction, inputs=[hidden_text, hidden_pred, correction_dropdown], outputs=correction_status)
+
+if __name__ == "__main__":
+    demo.launch()