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import gradio as gr
import pandas as pd
import re
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import LabelBinarizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, f1_score
# Define your functions and logic here
def load_and_prepare_data():
try:
file_path = 'WELFake_Dataset.csv' # Ensure this is the correct path
dataset = pd.read_csv(file_path)
print(f"Dataset loaded with {dataset.shape[0]} records")
dataset = dataset.drop(columns=['Unnamed: 0'])
dataset = dataset.dropna(subset=['title', 'text'])
dataset['clean_text'] = dataset['text'].apply(clean_text)
print(f"Dataset cleaned. Records after cleaning: {dataset.shape[0]}")
return dataset
except Exception as e:
return f"Error loading and preparing data: {e}"
def clean_text(text):
try:
text = re.sub(r'\W', ' ', text)
text = re.sub(r'\s+', ' ', text)
text = re.sub(r'\d', '', text)
text = text.lower().strip()
return text
except Exception as e:
return f"Error cleaning text: {e}"
def train_model(dataset):
try:
X_train, X_test, y_train, y_test = train_test_split(dataset['clean_text'], dataset['label'], test_size=0.2, random_state=42)
print(f"Training data size: {X_train.shape[0]}, Test data size: {X_test.shape[0]}")
vectorizer = TfidfVectorizer(max_features=10000)
X_train_tfidf = vectorizer.fit_transform(X_train)
X_test_tfidf = vectorizer.transform(X_test)
lb = LabelBinarizer()
y_train_binary = lb.fit_transform(y_train)
y_test_binary = lb.transform(y_test)
log_reg_model = LogisticRegression(max_iter=1000)
log_reg_model.fit(X_train_tfidf, y_train)
y_pred_log_reg_train = log_reg_model.predict(X_train_tfidf)
train_accuracy_log_reg = accuracy_score(y_train, y_pred_log_reg_train)
train_f1_log_reg = f1_score(y_train, y_pred_log_reg_train)
y_pred_log_reg = log_reg_model.predict(X_test_tfidf)
accuracy_log_reg = accuracy_score(y_test, y_pred_log_reg)
f1_log_reg = f1_score(y_test, y_pred_log_reg)
print(f"Train Accuracy: {train_accuracy_log_reg}, Train F1 Score: {train_f1_log_reg}")
print(f"Test Accuracy: {accuracy_log_reg}, Test F1 Score: {f1_log_reg}")
return vectorizer, lb, log_reg_model, accuracy_log_reg, f1_log_reg
except Exception as e:
return f"Error training model: {e}"
def fake_news_detection(text):
try:
dataset = load_and_prepare_data()
if isinstance(dataset, str): # Check if there was an error in loading data
return dataset
vectorizer, lb, log_reg_model, accuracy_log_reg, f1_log_reg = train_model(dataset)
if isinstance(vectorizer, str): # Check if there was an error in training models
return vectorizer
clean_text_input = clean_text(text)
text_tfidf = vectorizer.transform([clean_text_input])
prediction = log_reg_model.predict_proba(text_tfidf)
result = "Real" if prediction[0][1] >= 0.5 else "Fake"
return f"Prediction: {result}"
except Exception as e:
return f"Error in fake news detection: {e}"
iface = gr.Interface(
fn=fake_news_detection,
inputs=gr.Textbox(lines=2, placeholder="Enter news text here..."),
outputs="text",
title="Fake News Detector",
description="Enter a news headline or article text to check if it is fake or real."
)
if __name__ == "__main__":
iface.launch()
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