Create app.py

app.py

@@ -0,0 +1,33 @@
+import gradio as gr
+import numpy as np
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+import pickle
+import re
+
+# Load model and tokenizer
+model = load_model("best_GRU_tuning_model.h5")
+with open("my_tokenizer.pkl","rb") as f:
+    tokenizer = pickle.load(f)
+
+
+def preprocess_text(text):
+    text = text.lower()
+    text = re.sub(r'[^a-zA-Z\s]', '', text).strip()
+    return text
+
+
+def predict_sentiment(raw_text):
+    cleaned = preprocess_text(raw_text)
+    seq = tokenizer.texts_to_sequences([cleaned])
+    padded_seq = pad_sequences(seq, maxlen=200)
+    probs = model.predict(padded_seq)
+    predicted_class = np.argmax(probs, axis=1)[0]
+    rating = predicted_class + 1
+    return f"Predicted rating: {rating} (probabilities={probs[0]})"
+
+
+demo = gr.Interface(fn=predict_sentiment,
+                    inputs="text",
+                    outputs="label")
+demo.launch()