app.py

import torch
import torch.nn as nn
import torch.optim as optim
import pandas as pd
from collections import Counter
from sklearn.preprocessing import LabelEncoder
from torch.utils.data import Dataset, DataLoader
import pickle
import re
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import gradio as gr
import os
import nltk

# Download NLTK resources
nltk.download("stopwords", quiet=True)
nltk.download("wordnet", quiet=True)

# Initialize stopwords and lemmatizer globally
stop_words = set(stopwords.words("english"))
lemmatizer = WordNetLemmatizer()

# Device configuration
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Dataset Class
class AmazonReviewDataset(Dataset):
    def __init__(self, csv_file, max_length=50, sample_fraction=0.01, max_vocab_size=5000):
        # Load dataset
        print("Loading dataset from:", csv_file)
        self.data = pd.read_csv(csv_file, header=None, names=["label", "title", "text"])
        self.data = self.data.sample(frac=sample_fraction, random_state=42).reset_index(drop=True)
        print(f"Using {len(self.data)} samples ({sample_fraction * 100:.2f}% of the dataset).")
        
        # Clean text data
        self.data["text"] = self.data["text"].apply(self.clean_text)
        
        # Parameters
        self.max_length = max_length
        self.vocab = {"<PAD>": 0, "<UNK>": 1}
        self.label_encoder = LabelEncoder()
        
        # Build vocabulary
        print("Building vocabulary...")
        self._build_vocab(max_vocab_size)
        print("Vocabulary built successfully.")

        # Fit the label encoder
        self.label_encoder.fit(self.data["label"])

    def clean_text(self, text):
        # Remove special characters and numbers
        text = re.sub(r"[^a-zA-Z\s]", "", text)
        # Convert to lowercase
        text = text.lower()
        # Remove stopwords
        text = " ".join([word for word in text.split() if word not in stop_words])
        # Apply lemmatization
        text = " ".join([lemmatizer.lemmatize(word) for word in text.split()])
        return text

    def _build_vocab(self, max_vocab_size):
        # Combine title and text columns
        all_text = self.data["title"].astype(str) + " " + self.data["text"].astype(str)
        all_text = all_text.fillna("")  # Ensure no NaN values
        all_text = all_text[:50000]  # Use only the first 50,000 rows
        
        # Tokenize and build vocabulary in smaller chunks
        token_counts = Counter()
        chunk_size = 5000  # Process smaller chunks
        for i in range(0, len(all_text), chunk_size):
            chunk = all_text[i:i + chunk_size]
            tokens = " ".join(chunk).split()  # Tokenize the chunk
            token_counts.update(tokens)
            print(f"Processed {min(i + chunk_size, len(all_text))} rows...")
        
        # Keep only the most common tokens
        most_common_tokens = [token for token, _ in token_counts.most_common(max_vocab_size)]
        for token in most_common_tokens:
            self.vocab[token] = len(self.vocab)

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        label = self.data.iloc[idx]["label"]
        title = str(self.data.iloc[idx]["title"])
        text = str(self.data.iloc[idx]["text"])
        combined_text = title + " " + text  # Concatenate title and text
        tokens = combined_text.split()[:self.max_length]  # Tokenize and truncate
        token_ids = [self.vocab.get(token, self.vocab["<UNK>"]) for token in tokens]  # Convert tokens to IDs
        padding = [self.vocab["<PAD>"]] * (self.max_length - len(token_ids))  # Add padding
        token_ids += padding
        label_encoded = self.label_encoder.transform([label])[0]  # Encode label
        return torch.tensor(token_ids, dtype=torch.long).to(device), torch.tensor(label_encoded, dtype=torch.long).to(device)


# Policy Network
class PolicyNetwork(nn.Module):
    def __init__(self, vocab_size, embed_dim=32, hidden_dim=128, num_classes=2):
        super(PolicyNetwork, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embed_dim)
        self.lstm = nn.LSTM(embed_dim, hidden_dim, batch_first=True, bidirectional=True)
        self.fc = nn.Linear(hidden_dim * 2, num_classes)  # Bidirectional LSTM doubles hidden size

    def forward(self, x):
        embedded = self.embedding(x)
        lstm_out, _ = self.lstm(embedded)
        out = self.fc(lstm_out[:, -1, :])  # Use the last hidden state
        return out


# Training Function
def train_rl_model(dataset, policy_net, optimizer, num_episodes=3, entropy_weight=0.01, lr=0.001, batch_size=16):
    dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=4)
    for episode in range(num_episodes):
        print(f"Episode {episode + 1} started.")
        total_reward = 0
        for batch in dataloader:
            tokenized_reviews, true_labels = batch
            logits = policy_net(tokenized_reviews)
            probs = torch.softmax(logits, dim=-1)
            actions = torch.multinomial(probs, 1).squeeze()

            # Define rewards based on correctness
            rewards = [1 if action == label else -1 for action, label in zip(actions, true_labels)]
            rewards_tensor = torch.tensor(rewards, dtype=torch.float32).to(device)
            rewards_tensor = (rewards_tensor - rewards_tensor.mean()) / (rewards_tensor.std() + 1e-8)  # Normalize rewards

            # Compute loss
            loss = 0
            entropy_loss = 0
            for i, action in enumerate(actions):
                log_prob = torch.log(probs[i, action] + 1e-8)
                loss += -log_prob * rewards_tensor[i]
                entropy_loss += -(probs[i] * torch.log(probs[i] + 1e-8)).sum()

            loss += entropy_weight * entropy_loss

            # Backpropagation
            optimizer.zero_grad()
            loss.backward()
            torch.nn.utils.clip_grad_norm_(policy_net.parameters(), max_norm=1.0)
            optimizer.step()

            total_reward += sum(rewards)

        print(f"Episode {episode + 1}, Total Reward: {total_reward}, Loss: {loss.item()}")

    # Save the trained model
    with open("policy_net.pkl", "wb") as f:
        pickle.dump(policy_net.state_dict(), f)
    print("Model saved successfully as policy_net.pkl")


# Evaluation Function
def evaluate_model(dataset, policy_net):
    dataloader = DataLoader(dataset, batch_size=16, shuffle=False, num_workers=4)
    correct = 0
    total = 0
    policy_net.eval()
    with torch.no_grad():
        for batch in dataloader:
            tokenized_reviews, true_labels = batch
            logits = policy_net(tokenized_reviews)
            probs = torch.softmax(logits, dim=-1)
            predicted_classes = torch.argmax(probs, dim=-1)
            correct += (predicted_classes == true_labels).sum().item()
            total += true_labels.size(0)
    accuracy = correct / total
    print(f"Accuracy: {accuracy * 100:.2f}%")
    return accuracy


# Prediction Function for Gradio
def predict_review(review_text):
    with open("vocab.pkl", "rb") as f:
        vocab = pickle.load(f)
    with open("label_encoder.pkl", "rb") as f:
        label_encoder = pickle.load(f)

    tokenized_input = review_text.split()[:50]  # Limit to max length
    token_ids = [vocab.get(word, vocab["<UNK>"]) for word in tokenized_input]
    padding = [vocab["<PAD>"]] * (50 - len(token_ids))  # Pad if shorter than max length
    token_ids += padding
    token_ids = torch.tensor(token_ids).unsqueeze(0).to(device)

    policy_net = PolicyNetwork(len(vocab), embed_dim=32, hidden_dim=128, num_classes=2).to(device)
    with open("policy_net.pkl", "rb") as f:
        policy_net.load_state_dict(pickle.load(f))
    policy_net.eval()

    with torch.no_grad():
        logits = policy_net(token_ids)
        probs = torch.softmax(logits, dim=-1)
        predicted_class = torch.argmax(probs, dim=-1).item()
    predicted_label = label_encoder.inverse_transform([predicted_class])[0]
    return predicted_label


# Main Program
if __name__ == "__main__":
    train_csv_path = r"D:\b\train.csv"
    test_csv_path = r"D:\b\test.csv"
    sample_fraction = 0.01
    max_vocab_size = 5000
    num_episodes = 3
    batch_size = 16
    lr = 0.001
    entropy_weight = 0.01

    # Initialize datasets
    train_dataset = AmazonReviewDataset(train_csv_path, sample_fraction=sample_fraction, max_vocab_size=max_vocab_size)
    test_dataset = AmazonReviewDataset(test_csv_path, sample_fraction=sample_fraction, max_vocab_size=max_vocab_size)
    print("Dataset loaded successfully.")

    # Initialize model and optimizer
    policy_net = PolicyNetwork(len(train_dataset.vocab), embed_dim=32, hidden_dim=128, num_classes=2).to(device)
    optimizer = optim.Adam(policy_net.parameters(), lr=lr)

    # Train the model
    train_rl_model(train_dataset, policy_net, optimizer, num_episodes=num_episodes, entropy_weight=entropy_weight, lr=lr, batch_size=batch_size)

    # Evaluate the model
    evaluate_model(test_dataset, policy_net)

    # Save vocabulary and label encoder
    with open("vocab.pkl", "wb") as f:
        pickle.dump(train_dataset.vocab, f)
    with open("label_encoder.pkl", "wb") as f:
        pickle.dump(train_dataset.label_encoder, f)
    print("Vocabulary and label encoder saved successfully.")

    # Launch Gradio interface
    iface = gr.Interface(
        fn=predict_review,
        inputs="text",
        outputs="text",
        title="Amazon Review Sentiment Analysis",
        description="Enter a review to predict its sentiment (Positive/Negative)."    )

    iface.launch(share=True)