app.py

import gradio as gr
import numpy as np
import pickle
import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.initializers import Orthogonal
from tensorflow.keras.optimizers import Adam

# Load the trained model
custom_objects = {'Orthogonal': Orthogonal, 'Adam': Adam}
model = load_model('sentiment_analysis_model.h5', custom_objects=custom_objects)

# Load the tokenizer
with open('tokenizer.pickle', 'rb') as handle:
    tokenizer = pickle.load(handle)

# Define the max sequence length (as used during training)
max_seq_length = 100  # Adjust this based on your training setup

# Sentiment mapping
sentiment_mapping = {0: "Negative", 1: "Neutral", 2: "Positive"}

def classify_sentiment(text):
    # Preprocess the text (tokenization, padding, etc.)
    text_sequence = tokenizer.texts_to_sequences([text])
    padded_sequence = pad_sequences(text_sequence, maxlen=max_seq_length)

    # Make prediction using the trained model
    prediction = model.predict(padded_sequence)

    # Convert prediction to class label
    predicted_label = np.argmax(prediction)

    # Map class label to sentiment
    sentiment = sentiment_mapping[predicted_label]

    return sentiment

# Gradio interface
interface = gr.Interface(
    fn=classify_sentiment,
    inputs=gr.inputs.Textbox(lines=2, placeholder="Enter a sentence..."),
    outputs="text",
    title="Sentiment Analysis",
    description="Enter a sentence to classify its sentiment."
)

if __name__ == "__main__":
    interface.launch()