app.py

import pandas as pd
import numpy as np
import torch
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from torch.utils.data import Dataset, DataLoader
import gradio as gr

true_news = pd.read_csv('True.csv')
fake_news = pd.read_csv('Fake.csv')

true_news['label'] = 1
fake_news['label'] = 0
df = pd.concat([true_news, fake_news], ignore_index=True)

import re
import nltk
from nltk.corpus import stopwords
nltk.download('stopwords')

def preprocess_text(text):
    # Remove special characters
    text = re.sub(r'[^a-zA-Z\s]', '', text)
    # Convert to lowercase
    text = text.lower()
    # Remove stopwords
    stop_words = set(stopwords.words('english'))
    text = ' '.join([word for word in text.split() if word not in stop_words])
    return text

df['cleaned_text'] = df['text'].apply(preprocess_text)

vectorizer = TfidfVectorizer(max_features=5000)
X = vectorizer.fit_transform(df['cleaned_text']).toarray()
y = df['label'].values

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

class NewsDataset(Dataset):
    def __init__(self, X, y):
        self.X = torch.FloatTensor(X)
        self.y = torch.LongTensor(y)
    
    def __len__(self):
        return len(self.y)
    
    def __getitem__(self, idx):
        return self.X[idx], self.y[idx]

train_dataset = NewsDataset(X_train, y_train)
test_dataset = NewsDataset(X_test, y_test)

train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)

class FakeNewsDetector(torch.nn.Module):
    def __init__(self, input_dim):
        super(FakeNewsDetector, self).__init__()
        self.fc1 = torch.nn.Linear(input_dim, 64)
        self.fc2 = torch.nn.Linear(64, 16)
        self.fc3 = torch.nn.Linear(16, 2)
        self.relu = torch.nn.ReLU()
    
    def forward(self, x):
        x = self.relu(self.fc1(x))
        x = self.relu(self.fc2(x))
        x = self.fc3(x)
        return x

model = FakeNewsDetector(X_train.shape[1])
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

num_epochs = 10

for epoch in range(num_epochs):
    model.train()
    for batch_X, batch_y in train_loader:
        optimizer.zero_grad()
        outputs = model(batch_X)
        loss = criterion(outputs, batch_y)
        loss.backward()
        optimizer.step()
    
    # Evaluation
    model.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for batch_X, batch_y in test_loader:
            outputs = model(batch_X)
            _, predicted = torch.max(outputs.data, 1)
            total += batch_y.size(0)
            correct += (predicted == batch_y).sum().item()
    
    accuracy = 100 * correct / total
    print(f'Epoch [{epoch+1}/{num_epochs}], Accuracy: {accuracy:.2f}%')

def predict_fake_news(text):
    cleaned = preprocess_text(text)
    vectorized = vectorizer.transform([cleaned]).toarray()
    tensor = torch.FloatTensor(vectorized)
    
    model.eval()
    with torch.no_grad():
        output = model(tensor)
        _, predicted = torch.max(output.data, 1)
    
    return "Fake News" if predicted.item() == 0 else "True News"

# Example usage
example_text = "Scientists discover new planet capable of supporting life"
prediction = predict_fake_news(example_text)
print(f"Prediction: {prediction}")

# Gradio Interface
def gradio_interface(text):
    prediction = predict_fake_news(text)
    return prediction

iface = gr.Interface(
    fn=gradio_interface, 
    inputs="text", 
    outputs="text",
    title="Fake News Detector",
    description="Enter a news headline or text to predict whether it is Fake News or True News."
)

if __name__ == "__main__":
    iface.launch(share=True)