Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import tensorflow as tf
+import pickle
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+
+# Load model and tokenizer
+model = tf.keras.models.load_model("sentiment_model.h5")
+with open("tokenizer.pkl", "rb") as f:
+    tokenizer = pickle.load(f)
+
+max_len = 40
+
+def predict_sentiment(text):
+    seq = tokenizer.texts_to_sequences([text])
+    padded = pad_sequences(seq, maxlen=max_len, padding='post')
+    pred = model.predict(padded)[0][0]
+    label = "Positive" if pred >= 0.5 else "Negative"
+    return {label: float(pred) if label == "Positive" else 1 - float(pred)}
+
+# Gradio UI
+demo = gr.Interface(fn=predict_sentiment,
+                    inputs=gr.Textbox(lines=2, placeholder="Enter a tweet..."),
+                    outputs=gr.Label(num_top_classes=2),
+                    title="Sentiment Analysis on Tweets",
+                    description="Enter a tweet and get predicted sentiment (Positive/Negative) and confidence score.")
+
+demo.launch()