Create app.py

app.py

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+import zipfile
+import os
+import gradio as gr
+import tensorflow as tf
+from transformers import BertTokenizer, TFBertForSequenceClassification
+import numpy as np
+
+# Unzip model.zip if not already extracted
+if not os.path.exists("model"):
+    with zipfile.ZipFile("model.zip", 'r') as zip_ref:
+        zip_ref.extractall("model")
+
+# Correct Model Path
+MODEL_PATH = "model"
+
+# Load model and tokenizer
+model = TFBertForSequenceClassification.from_pretrained(MODEL_PATH)
+tokenizer = BertTokenizer.from_pretrained(MODEL_PATH)
+
+# Prediction function
+def predict_value(text, reason, threshold=0.7):
+    combined_text = text + " [SEP] " + reason
+    encoding = tokenizer(combined_text, padding="max_length", truncation=True, max_length=128, return_tensors="tf")
+
+    logits = model.predict(dict(encoding)).logits
+    probs = tf.nn.softmax(logits, axis=1).numpy()
+    
+    prediction = 1 if probs[:, 1] > threshold else 0
+    confidence = probs[:, 1][0]
+
+    if prediction == 1:
+        result = "✅ Valuable Feedback"
+    else:
+        result = "❌ Not Valuable Feedback"
+        
+    return result, f"Confidence Score: {confidence:.2f}"
+
+# Gradio UI
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+        # 🚀 Text & Reason Evaluator
+        Analyze if the provided text and reason are valuable!
+        """
+    )
+
+    with gr.Row():
+        text_input = gr.Textbox(label="📝 Enter the Text")
+        reason_input = gr.Textbox(label="💡 Enter the Reason")
+
+    predict_button = gr.Button("🔍 Predict")
+
+    output_result = gr.Textbox(label="Result")
+    output_confidence = gr.Textbox(label="Confidence Score")
+
+    predict_button.click(
+        predict_value,
+        inputs=[text_input, reason_input],
+        outputs=[output_result, output_confidence],
+    )
+
+demo.launch()