Delete app.py

app.py

@@ -1,76 +0,0 @@
-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()