Upload app.py

app.py

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+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()