adding app.py file

app.py

@@ -0,0 +1,83 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+from datetime import datetime
+import csv
+import os
+
+# Load model and tokenizer
+model_path = "model"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+
+label_map = {0: "Negative", 1: "Neutral", 2: "Positive"}
+colors = {"Negative": "red", "Neutral": "gray", "Positive": "green"}
+FEEDBACK_FILE = "user_feedback.csv"
+
+def predict_sentiment(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=256)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = torch.softmax(outputs.logits, dim=1).squeeze()
+        predicted_class = torch.argmax(probs).item()
+
+    label = label_map[predicted_class]
+    confidence = probs[predicted_class].item()
+    warning = "<br><span style='color:orange'>⚠️ Low confidence. Try rephrasing the review.</span>" if confidence < 0.5 else ""
+
+    result_html = f"""
+    <div style="border: 2px solid {colors[label]}; padding: 10px; border-radius: 10px;">
+        <h3 style='margin-bottom: 5px;'>Prediction: <span style='color:{colors[label]}'>{label}</span></h3>
+        <p>Confidence: {confidence:.2%}</p>
+        {warning}
+    </div>
+    """
+    return result_html, label, confidence
+
+def save_feedback(label, confidence, correct):
+    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    file_exists = os.path.isfile(FEEDBACK_FILE)
+    with open(FEEDBACK_FILE, mode="a", newline="", encoding="utf-8") as file:
+        writer = csv.writer(file)
+        if not file_exists:
+            writer.writerow(["timestamp", "predicted_label", "confidence", "correct_prediction"])
+        writer.writerow([timestamp, label, f"{confidence:.2%}", correct])
+    return "✅ Thanks for your feedback!"
+
+with gr.Blocks(title="Amazon Review Sentiment App") as demo:
+    gr.Markdown(
+        "<div style='text-align: center; font-size: 24px;'>📦 <b>Amazon Review Sentiment Classifier</b></div>"
+    )
+
+    gr.Markdown("Enter a review below to check if it's **Positive**, **Neutral**, or **Negative**.")
+
+    with gr.Row():
+        review_input = gr.Textbox(lines=10, placeholder="Type or paste a review here...", label="Your Review")
+        output_box = gr.HTML(label="Prediction Result")
+
+    predict_btn = gr.Button("🔍 Predict Sentiment")
+    hidden_label = gr.Textbox(visible=False)
+    hidden_conf = gr.Number(visible=False)
+
+    with gr.Row():
+        yes_btn = gr.Button("👍 Yes")
+        no_btn = gr.Button("👎 No")
+
+    feedback_output = gr.Textbox(label="Feedback Status", interactive=False)
+
+    predict_btn.click(fn=predict_sentiment, inputs=[review_input], outputs=[output_box, hidden_label, hidden_conf])
+    yes_btn.click(fn=save_feedback, inputs=[hidden_label, hidden_conf, gr.Textbox(value="yes", visible=False)], outputs=feedback_output)
+    no_btn.click(fn=save_feedback, inputs=[hidden_label, hidden_conf, gr.Textbox(value="no", visible=False)], outputs=feedback_output)
+
+    gr.Examples(
+        examples=[
+            "This phone exceeded all my expectations.",
+            "Battery life is just okay, not great.",
+            "Worst product I've ever purchased.",
+            "Highly recommended!",
+            "Meh. It's just fine, nothing special."
+        ],
+        inputs=review_input
+    )
+
+demo.launch(debug=True)