app.py

import gradio as gr
import numpy as np
import tensorflow as tf
import joblib
from tensorflow.keras.preprocessing.sequence import pad_sequences

# Load model and tokenizer
model = tf.keras.models.load_model("sentiment_model.h5")
tokenizer = joblib.load("tokenizer.joblib")

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()