app.py

import gradio as gr
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
import pandas as pd
import re
import string
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from keras.models import load_model
import nltk
import cloudpickle

# Download required NLTK data
nltk.download('stopwords')
nltk.download('punkt')

# Load the pre-trained model
model = load_model('Sarcasmmodel.h5')

with open('tokenizer.pkl', 'rb') as file:
    tokenizer_obj = cloudpickle.load(file)

# Function to clean the text
def clean_text(text):
    text = text.lower()
    text = re.sub(r"http\S+|www\S+|https\S+", '', text, flags=re.MULTILINE)
    text = re.sub(r'\@\w+|\#', '', text)
    text = text.translate(str.maketrans('', '', string.punctuation))
    text = re.sub(r'\d+', '', text)
    return text

# Function to tokenize and clean the text data
def CleanTokenize(df):
    head_lines = []
    lines = df["headline"].values.tolist()
    for line in lines:
        line = clean_text(line)
        tokens = word_tokenize(line)
        words = [word for word in tokens if word.isalpha()]
        stop_words = set(stopwords.words("english"))
        words = [w for w in words if not w in stop_words]
        head_lines.append(words)
    return head_lines

# Function to predict sarcasm
def predict_sarcasm(text, max_length=25):
    x_final = pd.DataFrame({"headline": [text]})
    test_lines = CleanTokenize(x_final)
    test_sequences = tokenizer_obj.texts_to_sequences(test_lines)
    test_review_pad = pad_sequences(test_sequences, maxlen=max_length, padding='post')
    pred = model.predict(test_review_pad)
    confidence = pred[0][0] * 100  # Convert to percentage
    result = "It's a sarcasm!" if confidence >= 50 else "It's not a sarcasm."
    return f"**Result:** {result}\n**Confidence:** {confidence:.2f}%"

# Gradio interface
def gradio_interface(text):
    return predict_sarcasm(text)

# Create the Gradio app
iface = gr.Interface(
    fn=gradio_interface,
    inputs=gr.Textbox(lines=2, placeholder="Type something sarcastic...", label="Input Text"),
    outputs=gr.Textbox(label="Prediction"),
    title="🤖 Sarcasm Detection",
    description="This app detects whether a given text is sarcastic or not.",
    examples=[
        ["Oh great, another Monday morning!"],
        ["I just love spending hours in traffic."],
        ["This is the best day of my life!"]
    ],
    theme="default"
)

# Launch the app
iface.launch()