app.py

import gradio as gr
import numpy as np
import json
import re
import random
from typing import List, Dict, Tuple, Optional
import os
import time
import matplotlib.pyplot as plt
from io import BytesIO
import base64
from datetime import datetime

# Assuming all the classes (ActivationFunctions, LossFunctions, Layer, DenseLayer,
# DropoutLayer, NeuralNetwork, TextProcessor, Chatbot) are defined as in your uploaded code
# I'm not repeating them here for brevity


class ActivationFunctions:
    """Class containing various activation functions and their derivatives."""

    @staticmethod
    def sigmoid(z: np.ndarray) -> np.ndarray:
        """Sigmoid activation function."""
        z = np.clip(z, -500, 500)
        return 1 / (1 + np.exp(-z))

    @staticmethod
    def sigmoid_derivative(z: np.ndarray) -> np.ndarray:
        """Derivative of the sigmoid function."""
        s = ActivationFunctions.sigmoid(z)
        return s * (1 - s)

    @staticmethod
    def relu(z: np.ndarray) -> np.ndarray:
        """ReLU activation function."""
        return np.maximum(0, z)

    @staticmethod
    def relu_derivative(z: np.ndarray) -> np.ndarray:
        """Derivative of the ReLU function."""
        return np.where(z > 0, 1, 0)

    @staticmethod
    def softmax(z: np.ndarray) -> np.ndarray:
        """Softmax activation function."""
        exp_z = np.exp(z - np.max(z))
        return exp_z / exp_z.sum(axis=0, keepdims=True)


class LossFunctions:
    """Class containing various loss functions and their derivatives."""

    @staticmethod
    def mse(output: np.ndarray, target: np.ndarray) -> float:
        """Mean Squared Error loss."""
        return np.mean((output - target) ** 2)

    @staticmethod
    def mse_derivative(output: np.ndarray, target: np.ndarray) -> np.ndarray:
        """Derivative of MSE loss."""
        return 2 * (output - target) / output.size

    @staticmethod
    def cross_entropy(output: np.ndarray, target: np.ndarray) -> float:
        """Cross Entropy loss for multi-class classification."""
        epsilon = 1e-15
        output = np.clip(output, epsilon, 1 - epsilon)
        return -np.sum(target * np.log(output)) / output.shape[1]

    @staticmethod
    def cross_entropy_derivative(output: np.ndarray, target: np.ndarray) -> np.ndarray:
        """Derivative of Cross Entropy loss."""
        epsilon = 1e-15
        output = np.clip(output, epsilon, 1 - epsilon)
        return -target / output / output.shape[1]


class Layer:
    """Base class for neural network layers."""

    def forward(self, inputs: np.ndarray) -> np.ndarray:
        """Forward pass through the layer."""
        raise NotImplementedError

    def backward(self, grad: np.ndarray) -> np.ndarray:
        """Backward pass through the layer."""
        raise NotImplementedError

    def update(self, learning_rate: float) -> None:
        """Update layer parameters."""
        pass

    def get_parameters(self) -> List:
        """Get layer parameters."""
        return []


class DenseLayer(Layer):
    """Fully connected layer with improved numerical stability."""

    def __init__(self, input_size: int, output_size: int, activation: str = "sigmoid"):
        """Initialize the dense layer with more stable parameters."""
        self.input_size = input_size
        self.output_size = output_size

        # Use smaller initialization to prevent exploding gradients
        # Xavier/Glorot initialization with smaller scale factor
        self.weights = np.random.randn(output_size, input_size) * np.sqrt(
            1 / (input_size + output_size)
        )
        self.biases = np.zeros((output_size, 1))

        # Set activation function
        if activation == "sigmoid":
            self.activation_fn = ActivationFunctions.sigmoid
            self.activation_derivative = ActivationFunctions.sigmoid_derivative
        elif activation == "relu":
            self.activation_fn = ActivationFunctions.relu
            self.activation_derivative = ActivationFunctions.relu_derivative
        elif activation == "softmax":
            self.activation_fn = ActivationFunctions.softmax
            self.activation_derivative = None
        else:
            raise ValueError(f"Unsupported activation function: {activation}")

        self.activation_name = activation

        # Cache for backward pass
        self.inputs = None
        self.z = None
        self.output = None

        # Gradients
        self.dW = None
        self.db = None

    def forward(self, inputs: np.ndarray) -> np.ndarray:
        """Forward pass through the layer with improved numerical stability."""
        self.inputs = inputs

        # Use dot product with better numerical stability
        self.z = np.dot(self.weights, inputs) + self.biases

        # Clip values to prevent overflow in activations
        if self.activation_name == "sigmoid":
            self.z = np.clip(self.z, -15, 15)  # Prevent overflow in sigmoid

        self.output = self.activation_fn(self.z)

        # Add small epsilon to prevent exact zeros or ones
        if self.activation_name == "softmax":
            epsilon = 1e-10
            self.output = np.clip(self.output, epsilon, 1.0 - epsilon)

        return self.output

    def backward(self, grad: np.ndarray) -> np.ndarray:
        """Backward pass through the layer with gradient clipping."""
        if self.activation_name == "softmax":
            # Special case for softmax + cross-entropy
            delta = grad
        else:
            delta = grad * self.activation_derivative(self.z)

        # Compute gradients
        self.dW = np.dot(delta, self.inputs.T)
        self.db = np.sum(delta, axis=1, keepdims=True)

        # Clip gradients to prevent exploding gradients
        max_grad_norm = 5.0
        self.dW = np.clip(self.dW, -max_grad_norm, max_grad_norm)
        self.db = np.clip(self.db, -max_grad_norm, max_grad_norm)

        # Gradient to pass to the previous layer
        return np.dot(self.weights.T, delta)

    def update(self, learning_rate: float) -> None:
        """Update layer parameters using gradient descent with weight decay."""
        # Add small weight decay to prevent overfitting
        weight_decay = 1e-4
        weight_decay_term = weight_decay * self.weights

        self.weights -= learning_rate * (self.dW + weight_decay_term)
        self.biases -= learning_rate * self.db


class DropoutLayer(Layer):
    """Dropout layer for regularization."""

    def __init__(self, dropout_rate: float = 0.5):
        """Initialize the dropout layer."""
        self.dropout_rate = dropout_rate
        self.mask = None

    def forward(self, inputs: np.ndarray, training: bool = True) -> np.ndarray:
        """Forward pass through the layer."""
        if not training:
            return inputs

        # Create dropout mask
        self.mask = np.random.binomial(1, 1 - self.dropout_rate, size=inputs.shape) / (
            1 - self.dropout_rate
        )
        return inputs * self.mask

    def backward(self, grad: np.ndarray) -> np.ndarray:
        """Backward pass through the layer."""
        return grad * self.mask


class NeuralNetwork:
    """Neural network with multiple layers."""

    def __init__(self):
        """Initialize the neural network."""
        self.layers = []
        self.loss_fn = None
        self.loss_derivative = None

    def add(self, layer: Layer) -> None:
        """Add a layer to the network."""
        self.layers.append(layer)

    def set_loss(self, loss_type: str) -> None:
        """Set the loss function."""
        if loss_type == "mse":
            self.loss_fn = LossFunctions.mse
            self.loss_derivative = LossFunctions.mse_derivative
        elif loss_type == "cross_entropy":
            self.loss_fn = LossFunctions.cross_entropy
            self.loss_derivative = LossFunctions.cross_entropy_derivative
        else:
            raise ValueError(f"Unsupported loss function: {loss_type}")

    def forward(self, x: np.ndarray, training: bool = True) -> np.ndarray:
        """Forward pass through the network."""
        output = x
        for layer in self.layers:
            if isinstance(layer, DropoutLayer):
                output = layer.forward(output, training)
            else:
                output = layer.forward(output)
        return output

    def compute_loss(self, y_pred: np.ndarray, y_true: np.ndarray) -> float:
        """Compute the loss."""
        return self.loss_fn(y_pred, y_true)

    def backward(self, y_pred: np.ndarray, y_true: np.ndarray) -> None:
        """Backward pass through the network."""
        # Initial gradient from the loss function
        grad = self.loss_derivative(y_pred, y_true)

        # Propagate gradient through layers in reverse order
        for layer in reversed(self.layers):
            grad = layer.backward(grad)

    def update(self, learning_rate: float) -> None:
        """Update network parameters."""
        for layer in self.layers:
            layer.update(learning_rate)

    def predict(self, x: np.ndarray) -> np.ndarray:
        """Make predictions."""
        return self.forward(x, training=False)

    @classmethod
    def load(cls, filename: str) -> "NeuralNetwork":
        """Load a model from a file."""
        with open(filename, "r") as f:
            model_data = json.load(f)

        network = cls()
        network.set_loss(model_data.get("loss_type", "cross_entropy"))

        for layer_data in model_data["layers"]:
            if layer_data["type"] == "dense":
                layer = DenseLayer(
                    layer_data["input_size"],
                    layer_data["output_size"],
                    layer_data["activation"],
                )
                layer.weights = np.array(layer_data["weights"])
                layer.biases = np.array(layer_data["biases"])
                network.add(layer)
            elif layer_data["type"] == "dropout":
                layer = DropoutLayer(layer_data["dropout_rate"])
                network.add(layer)

        return network

    def save(self, filename: str) -> None:
        """Save the model to a file."""
        model_data = {"layers": []}

        for layer in self.layers:
            if isinstance(layer, DenseLayer):
                layer_data = {
                    "type": "dense",
                    "input_size": layer.input_size,
                    "output_size": layer.output_size,
                    "activation": layer.activation_name,
                    "weights": layer.weights.tolist(),
                    "biases": layer.biases.tolist(),
                }
                model_data["layers"].append(layer_data)
            elif isinstance(layer, DropoutLayer):
                layer_data = {"type": "dropout", "dropout_rate": layer.dropout_rate}
                model_data["layers"].append(layer_data)

        with open(filename, "w") as f:
            json.dump(model_data, f)


class TextProcessor:
    """Class for processing text data."""

    def __init__(self):
        """Initialize the text processor."""
        self.vocabulary = []
        self.vocabulary_size = 0

    def tokenize(self, sentence: str) -> List[str]:
        """Tokenize a sentence."""
        return re.findall(r"\w+", sentence.lower())

    def build_vocabulary(self, sentences: List[str]) -> None:
        """Build the vocabulary from a list of sentences."""
        vocabulary = set()
        for sentence in sentences:
            tokens = self.tokenize(sentence)
            vocabulary.update(tokens)

        self.vocabulary = sorted(list(vocabulary))
        self.vocabulary_size = len(self.vocabulary)

    def sentence_to_bow(self, sentence: str) -> np.ndarray:
        """Convert a sentence to a bag-of-words vector."""
        tokens = self.tokenize(sentence)
        vector = np.zeros((self.vocabulary_size, 1))

        for token in tokens:
            if token in self.vocabulary:
                idx = self.vocabulary.index(token)
                vector[idx, 0] = 1

        return vector

    def save(self, filename: str) -> None:
        """Save the text processor to a file."""
        processor_data = {
            "vocabulary": self.vocabulary,
            "vocabulary_size": self.vocabulary_size,
        }

        with open(filename, "w") as f:
            json.dump(processor_data, f)

    @classmethod
    def load(cls, filename: str) -> "TextProcessor":
        """Load a text processor from a file."""
        with open(filename, "r") as f:
            processor_data = json.load(f)

        processor = cls()
        processor.vocabulary = processor_data["vocabulary"]
        processor.vocabulary_size = processor_data["vocabulary_size"]

        return processor


class Chatbot:
    """Neural network based chatbot."""

    def __init__(self):
        """Initialize the chatbot."""
        self.intents = {}
        self.text_processor = TextProcessor()
        self.model = NeuralNetwork()
        self.intent_names = []
        self.confidence_threshold = 0.5
        self.default_response = "I'm not sure I understand. Could you rephrase that?"
        self.training_history = None

    def load_intents(self, intents_data: Dict) -> None:
        """Load intents data."""
        self.intents = intents_data
        self.intent_names = list(self.intents.keys())

        # Extract all patterns for building vocabulary
        all_patterns = []
        for intent in self.intents.values():
            all_patterns.extend(intent["patterns"])

        # Build vocabulary from patterns
        self.text_processor.build_vocabulary(all_patterns)

    def load_intents_from_file(self, filename: str) -> None:
        """Load intents from a JSON file."""
        with open(filename, "r") as f:
            intents_data = json.load(f)

        self.load_intents(intents_data)

    def save_intents(self, filename: str) -> None:
        """Save intents to a JSON file."""
        with open(filename, "w") as f:
            json.dump(self.intents, f, indent=4)

    def load_model(self, filename: str) -> None:
        """Load a model from a file."""
        self.model = NeuralNetwork.load(filename)

    def save_model(self, filename: str) -> None:
        """Save the model to a file."""
        self.model.save(filename)
        # Also save the text processor and intent names
        self.text_processor.save(filename.replace(".json", "_processor.json"))

        # Save intent names
        with open(filename.replace(".json", "_intents.json"), "w") as f:
            json.dump(
                {
                    "intent_names": self.intent_names,
                    "confidence_threshold": self.confidence_threshold,
                    "default_response": self.default_response,
                },
                f,
            )

    def build_model(
        self, hidden_layers: List[int] = [8], dropout_rate: float = 0.0
    ) -> None:
        """Build the neural network model."""
        # Input layer size is the vocabulary size
        input_size = self.text_processor.vocabulary_size

        # Output layer size is the number of intents
        output_size = len(self.intent_names)

        if output_size == 0:
            raise ValueError("No intents loaded. Please load intents first.")

        # Create the model
        self.model = NeuralNetwork()

        # Add first hidden layer
        self.model.add(DenseLayer(input_size, hidden_layers[0], "relu"))

        # Add dropout if needed
        if dropout_rate > 0:
            self.model.add(DropoutLayer(dropout_rate))

        # Add additional hidden layers
        for i in range(1, len(hidden_layers)):
            self.model.add(DenseLayer(hidden_layers[i - 1], hidden_layers[i], "relu"))

            # Add dropout if needed
            if dropout_rate > 0:
                self.model.add(DropoutLayer(dropout_rate))

        # Add output layer with softmax activation for classification
        self.model.add(DenseLayer(hidden_layers[-1], output_size, "softmax"))

        # Set cross-entropy loss for classification
        self.model.set_loss("cross_entropy")

    def train(
        self,
        epochs: int = 1000,
        learning_rate: float = 0.01,
        batch_size: int = None,
        verbose: bool = True,
    ) -> Dict:
        """Train the model with numerical stability fixes."""
        # Prepare training data
        X_train = []
        y_train = []

        for idx, intent in enumerate(self.intent_names):
            for pattern in self.intents[intent]["patterns"]:
                # Convert pattern to bag-of-words
                X_train.append(self.text_processor.sentence_to_bow(pattern))

                # Create one-hot encoded target
                target = np.zeros((len(self.intent_names), 1))
                target[idx, 0] = 1
                y_train.append(target)

        # Convert to numpy arrays
        X_train = np.hstack(X_train)
        y_train = np.hstack(y_train)

        # Training history
        history = {"loss": [], "accuracy": []}

        # Apply gradient clipping to prevent exploding gradients
        max_grad_norm = 1.0

        # Training loop
        for epoch in range(epochs):
            # Forward pass
            outputs = self.model.forward(X_train)

            # Add small epsilon to prevent log(0)
            epsilon = 1e-10
            outputs = np.clip(outputs, epsilon, 1.0 - epsilon)

            # Compute loss
            loss = self.model.compute_loss(outputs, y_train)

            # Check for NaN and if found, break training
            if np.isnan(loss):
                if verbose:
                    print(f"NaN loss detected at epoch {epoch+1}. Stopping training.")

                # If we have previous good values, use those
                if epoch > 0:
                    break
                else:
                    # Otherwise, return with error
                    return {"loss": [0], "accuracy": [0]}

            # Backward pass
            self.model.backward(outputs, y_train)

            # Apply gradient clipping to each layer
            for layer in self.model.layers:
                if hasattr(layer, "dW") and layer.dW is not None:
                    # Clip gradients
                    layer.dW = np.clip(layer.dW, -max_grad_norm, max_grad_norm)
                if hasattr(layer, "db") and layer.db is not None:
                    layer.db = np.clip(layer.db, -max_grad_norm, max_grad_norm)

            # Update parameters
            self.model.update(learning_rate)

            # Compute accuracy
            predictions = np.argmax(outputs, axis=0)
            targets = np.argmax(y_train, axis=0)
            accuracy = np.mean(predictions == targets)

            # Save history
            history["loss"].append(
                float(loss)
            )  # Convert to Python float to ensure it's serializable
            history["accuracy"].append(float(accuracy))

            # Print progress
            if verbose and (epoch + 1) % 100 == 0:
                print(
                    f"Epoch {epoch + 1}/{epochs}, Loss: {loss:.4f}, Accuracy: {accuracy:.4f}"
                )

        self.training_history = history
        return history

    def predict(self, sentence: str) -> Tuple[str, float]:
        """Predict the intent of a sentence."""
        # Convert to bag-of-words
        bow = self.text_processor.sentence_to_bow(sentence)

        # Get prediction
        prediction = self.model.predict(bow)

        # Get predicted intent and confidence
        intent_idx = np.argmax(prediction)
        confidence = prediction[intent_idx, 0]

        return self.intent_names[intent_idx], confidence

    def get_response(self, sentence: str) -> Tuple[str, str, float]:
        """Get a response for a user input."""
        intent, confidence = self.predict(sentence)

        # Use default response if confidence is below threshold
        if confidence < self.confidence_threshold:
            return "unknown", self.default_response, confidence

        # Get a random response for the predicted intent
        responses = self.intents[intent]["responses"]
        response = random.choice(responses)

        return intent, response, confidence

    def plot_training_history(self, history: Dict = None) -> None:
        """Plot the training history."""
        if history is None:
            history = self.training_history

        if history is None:
            print("No training history available.")
            return

        plt.figure(figsize=(12, 5))

        plt.subplot(1, 2, 1)
        plt.plot(history["loss"])
        plt.title("Model Loss")
        plt.xlabel("Epoch")
        plt.ylabel("Loss")

        plt.subplot(1, 2, 2)
        plt.plot(history["accuracy"])
        plt.title("Model Accuracy")
        plt.xlabel("Epoch")
        plt.ylabel("Accuracy")

        plt.tight_layout()
        plt.show()

    def get_training_plot_as_base64(self, history: Dict = None) -> str:
        """Generate a base64 encoded image of the training history plot with improved error handling."""
        if history is None:
            history = self.training_history

        if history is None or "loss" not in history or len(history["loss"]) == 0:
            return None

        try:
            plt.figure(figsize=(12, 5))

            # Check for NaN values and filter them out
            loss_values = [x for x in history["loss"] if not np.isnan(x)]
            acc_values = [x for x in history["accuracy"] if not np.isnan(x)]

            if len(loss_values) == 0 or len(acc_values) == 0:
                return None

            # Plot loss (with error handling)
            plt.subplot(1, 2, 1)
            plt.plot(loss_values)
            plt.title("Model Loss")
            plt.xlabel("Epoch")
            plt.ylabel("Loss")

            # Plot accuracy (with error handling)
            plt.subplot(1, 2, 2)
            plt.plot(acc_values)
            plt.title("Model Accuracy")
            plt.xlabel("Epoch")
            plt.ylabel("Accuracy")

            plt.tight_layout()

            # Save plot to a BytesIO object
            buf = BytesIO()
            plt.savefig(buf, format="png")
            buf.seek(0)

            # Encode to base64
            img_str = base64.b64encode(buf.read()).decode("utf-8")

            plt.close()

            # Save the image to a file instead of returning the base64 string directly
            # This avoids the file name too long error
            img_path = "training_plot.png"
            with open(img_path, "wb") as f:
                f.write(base64.b64decode(img_str))

            return img_path
        except Exception as e:
            print(f"Error generating training plot: {str(e)}")
            return None

    def chat(self):
        """Start a chat session in the console."""
        print("Chatbot: Hello! Type 'quit' to exit.")

        while True:
            user_input = input("You: ")

            if user_input.lower() in ["quit", "exit", "bye"]:
                print("Chatbot: Goodbye!")
                break

            intent, response, confidence = self.get_response(user_input)
            print(f"Chatbot ({intent}, {confidence:.2f}): {response}")


# Initialize the chatbot
chatbot = Chatbot()

# Default intents
default_intents = {
    "greeting": {
        "patterns": ["Hi", "Hello", "Hey", "Good morning", "What's up"],
        "responses": ["Hello!", "Hi there!", "Greetings!", "Hey! How can I help you?"],
    },
    "farewell": {
        "patterns": ["Bye", "See you", "Goodbye", "Later", "I'm leaving"],
        "responses": ["Goodbye!", "See you later!", "Farewell!", "Take care!"],
    },
    "thanks": {
        "patterns": ["Thanks", "Thank you", "Much appreciated", "Appreciate it"],
        "responses": ["You're welcome!", "No problem!", "Anytime!", "Glad to help!"],
    },
    "help": {
        "patterns": ["Help", "I need help", "Can you help me", "Support"],
        "responses": [
            "How can I help you?",
            "I'm here to assist you.",
            "What do you need help with?",
        ],
    },
}


# Function to initialize the chatbot
def initialize_chatbot():
    global chatbot

    # Check if model exists
    model_path = "chatbot_model.json"
    processor_path = "chatbot_model_processor.json"
    intents_names_path = "chatbot_model_intents.json"
    intents_path = "intents.json"

    # Check if intents file exists
    if os.path.exists(intents_path):
        try:
            chatbot.load_intents_from_file(intents_path)
            print(f"Loaded intents from {intents_path}")
        except Exception as e:
            print(f"Error loading intents: {e}")
            print("Loading default intents")
            chatbot.load_intents(default_intents)
    else:
        print("No intents file found. Loading default intents")
        chatbot.load_intents(default_intents)
        # Save default intents
        chatbot.save_intents(intents_path)

    # Check if all model files exist
    if (
        os.path.exists(model_path)
        and os.path.exists(processor_path)
        and os.path.exists(intents_names_path)
    ):
        try:
            # Load the model
            chatbot.load_model(model_path)

            # Load the text processor
            chatbot.text_processor = TextProcessor.load(processor_path)

            # Load intent names and settings
            with open(intents_names_path, "r") as f:
                intents_data = json.load(f)
                chatbot.intent_names = intents_data["intent_names"]
                chatbot.confidence_threshold = intents_data.get(
                    "confidence_threshold", 0.5
                )
                chatbot.default_response = intents_data.get(
                    "default_response",
                    "I'm not sure I understand. Could you rephrase that?",
                )

            print(f"Loaded existing model from {model_path}")
        except Exception as e:
            print(f"Error loading model: {e}")
            print("A new model will be built and trained")
            chatbot.build_model(hidden_layers=[32, 16])
    else:
        print(
            "No model found or incomplete model files. A new model will be built and trained"
        )
        chatbot.build_model(hidden_layers=[32, 16])


# Call initialize
initialize_chatbot()

# Chat history for the interface
chat_history = []


# Function to respond to user messages
def respond(message, history):
    if not message:
        return "Please type a message."

    # Get response from chatbot
    intent, response, confidence = chatbot.get_response(message)

    # Add thinking animation (simulate processing)
    time.sleep(0.5)

    # Return the response
    return response


# Function to get intent and confidence
def get_intent_info(message):
    if not message:
        return "N/A", 0.0

    # Get intent and confidence
    intent, confidence = chatbot.predict(message)
    return intent, float(confidence)


# Function to add a new intent
def add_intent(intent_name, patterns, responses):
    if not intent_name or not patterns or not responses:
        return "Please fill all fields"

    # Split patterns and responses
    pattern_list = [p.strip() for p in patterns.split("\n") if p.strip()]
    response_list = [r.strip() for r in responses.split("\n") if r.strip()]

    if not pattern_list or not response_list:
        return "Please provide at least one pattern and one response"

    # Check if intent already exists
    if intent_name in chatbot.intents:
        # Update existing intent
        chatbot.intents[intent_name]["patterns"].extend(pattern_list)
        chatbot.intents[intent_name]["responses"].extend(response_list)
    else:
        # Add new intent
        chatbot.intents[intent_name] = {
            "patterns": pattern_list,
            "responses": response_list,
        }
        chatbot.intent_names.append(intent_name)

    # Save intents
    chatbot.save_intents("intents.json")

    return f"Intent '{intent_name}' added/updated successfully"


# Fixed train_model function with corrected format string
def train_model(epochs, learning_rate, hidden_layers_str, dropout_rate):
    try:
        # Parse hidden layers
        hidden_layers = [
            int(x.strip()) for x in hidden_layers_str.split(",") if x.strip()
        ]

        if not hidden_layers:
            return (
                "Error: Invalid hidden layer format. Use comma-separated numbers, e.g. '32,16'",
                None,
            )

        # Convert to float/int and use lower learning rate for stability
        epochs = int(epochs)
        learning_rate = min(
            float(learning_rate), 0.005
        )  # Cap learning rate for stability
        dropout_rate = float(dropout_rate)

        # Validate intents and vocabulary
        if len(chatbot.intent_names) < 2:
            return (
                "Error: Need at least 2 intents for training. Please add more intents.",
                None,
            )

        if chatbot.text_processor.vocabulary_size == 0:
            return (
                "Error: No vocabulary built. Please add more patterns to your intents.",
                None,
            )

        # Rebuild model with new architecture
        chatbot.build_model(hidden_layers=hidden_layers, dropout_rate=dropout_rate)

        # Train the model
        history = chatbot.train(
            epochs=epochs, learning_rate=learning_rate, verbose=True
        )

        # Check if training was successful
        if not history or "loss" not in history or not history["loss"]:
            return "Training failed - no history data returned", None

        # Format final loss and accuracy safely
        final_loss = history["loss"][-1] if history["loss"] else 0
        final_accuracy = history["accuracy"][-1] if history["accuracy"] else 0

        if np.isnan(final_loss):
            final_loss_str = "NaN"
        else:
            final_loss_str = f"{final_loss:.4f}"

        if np.isnan(final_accuracy):
            final_accuracy_str = "NaN"
        else:
            final_accuracy_str = f"{final_accuracy:.4f}"

        # Save the model
        chatbot.save_model("chatbot_model.json")

        # Generate plot image
        img_str = chatbot.get_training_plot_as_base64(history)

        return (
            f"Model trained successfully with:\n"
            f"- Epochs: {epochs}\n"
            f"- Learning Rate: {learning_rate}\n"
            f"- Hidden Layers: {hidden_layers}\n"
            f"- Dropout Rate: {dropout_rate}\n"
            f"- Final Loss: {final_loss_str}\n"
            f"- Final Accuracy: {final_accuracy_str}"
        ), img_str
    except Exception as e:
        import traceback

        error_details = traceback.format_exc()
        return f"Error training model: {str(e)}\n\nDetails:\n{error_details}", None


# Function to load an existing model
def load_model_from_file(file_obj):
    if not file_obj:
        return "No file uploaded"

    try:
        file_path = file_obj.name

        # Check file extension
        if not file_path.endswith(".json"):
            return "Please upload a JSON model file"

        # Load the model
        chatbot.load_model(file_path)

        # Get the base name without extension for related files
        base_name = os.path.splitext(file_path)[0]
        processor_path = f"{base_name}_processor.json"
        intents_names_path = f"{base_name}_intents.json"

        # Check for related files
        if os.path.exists(processor_path):
            chatbot.text_processor = TextProcessor.load(processor_path)

        if os.path.exists(intents_names_path):
            with open(intents_names_path, "r") as f:
                intents_data = json.load(f)
                chatbot.intent_names = intents_data["intent_names"]
                chatbot.confidence_threshold = intents_data.get(
                    "confidence_threshold", 0.5
                )
                chatbot.default_response = intents_data.get(
                    "default_response",
                    "I'm not sure I understand. Could you rephrase that?",
                )

        return f"Model loaded successfully from {file_path}"
    except Exception as e:
        return f"Error loading model: {str(e)}"


# Function to save the current model
def save_model():
    try:
        # Get timestamp for filename
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        filename = f"chatbot_model_{timestamp}.json"

        # Save the model
        chatbot.save_model(filename)

        return f"Model saved as {filename}"
    except Exception as e:
        return f"Error saving model: {str(e)}"


# Function to update settings
def update_settings(threshold, default_response):
    try:
        # Update settings
        chatbot.confidence_threshold = float(threshold)
        chatbot.default_response = default_response

        # Save settings to the model intents file
        with open("chatbot_model_intents.json", "w") as f:
            json.dump(
                {
                    "intent_names": chatbot.intent_names,
                    "confidence_threshold": chatbot.confidence_threshold,
                    "default_response": chatbot.default_response,
                },
                f,
            )

        return "Settings updated successfully"
    except Exception as e:
        return f"Error updating settings: {str(e)}"


# Function to list intents
def list_intents():
    if not chatbot.intents:
        return "No intents available"

    intents_info = ""
    for intent_name, intent_data in chatbot.intents.items():
        patterns = ", ".join(intent_data["patterns"][:3])
        if len(intent_data["patterns"]) > 3:
            patterns += "..."

        responses = ", ".join(intent_data["responses"][:3])
        if len(intent_data["responses"]) > 3:
            responses += "..."

        intents_info += f"**Intent**: {intent_name}\n"
        intents_info += f"**Patterns**: {patterns}\n"
        intents_info += f"**Responses**: {responses}\n\n"

    return intents_info


# Function to edit an intent
def edit_intent(intent_name, new_patterns, new_responses):
    if not intent_name or intent_name not in chatbot.intents:
        return f"Intent '{intent_name}' not found"

    # Split patterns and responses
    if new_patterns:
        pattern_list = [p.strip() for p in new_patterns.split("\n") if p.strip()]
        if pattern_list:
            chatbot.intents[intent_name]["patterns"] = pattern_list

    if new_responses:
        response_list = [r.strip() for r in new_responses.split("\n") if r.strip()]
        if response_list:
            chatbot.intents[intent_name]["responses"] = response_list

    # Save intents
    chatbot.save_intents("intents.json")

    return f"Intent '{intent_name}' updated successfully"


# Function to delete an intent
def delete_intent(intent_name):
    if not intent_name or intent_name not in chatbot.intents:
        return f"Intent '{intent_name}' not found"

    # Delete intent
    del chatbot.intents[intent_name]
    chatbot.intent_names.remove(intent_name)

    # Save intents
    chatbot.save_intents("intents.json")

    return f"Intent '{intent_name}' deleted successfully"


# Get the list of intents for dropdown
def get_intent_list():
    return chatbot.intent_names


# Function to export intents
def export_intents():
    try:
        # Get timestamp for filename
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        filename = f"intents_{timestamp}.json"

        # Save intents
        with open(filename, "w") as f:
            json.dump(chatbot.intents, f, indent=4)

        return f"Intents exported as {filename}"
    except Exception as e:
        return f"Error exporting intents: {str(e)}"


# Function to import intents
def import_intents_from_file(file_obj):
    if not file_obj:
        return "No file uploaded"

    try:
        file_path = file_obj.name

        # Check file extension
        if not file_path.endswith(".json"):
            return "Please upload a JSON intents file"

        # Load intents
        with open(file_path, "r") as f:
            intents_data = json.load(f)

        # Validate intents format
        for intent_name, intent_data in intents_data.items():
            if (
                not isinstance(intent_data, dict)
                or "patterns" not in intent_data
                or "responses" not in intent_data
            ):
                return f"Invalid intent format for '{intent_name}'"

        # Update chatbot intents
        chatbot.load_intents(intents_data)

        # Save intents
        chatbot.save_intents("intents.json")

        return f"Imported {len(intents_data)} intents from {file_path}"
    except Exception as e:
        return f"Error importing intents: {str(e)}"


# Function to get intent details
def get_intent_details(intent_name):
    if not intent_name or intent_name not in chatbot.intents:
        return "", ""

    patterns = "\n".join(chatbot.intents[intent_name]["patterns"])
    responses = "\n".join(chatbot.intents[intent_name]["responses"])

    return patterns, responses


# Create the Gradio interface with multiple tabs
with gr.Blocks(title="Neural Network Chatbot", theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 🤖 Neural Network Chatbot")
    gr.Markdown(
        """ This chatbot uses a neural network to understand and respond to your messages. 
        
        This chatbot application was developed by:

        | **Name** | **Student ID** | **Email** |
        |----------|----------------|-----------|
        | AARJEYAN SHRESTHA | C0927422 | C0927422@mylambton.ca |
        | PRAJWAL LUITEL | C0927658 | C0927658@mylambton.ca |
        | RAJAN GHIMIRE | C0924991 | C0924991@mylambton.ca |
        | RISHABH JHA | C0923563 | C0923563@mylambton.ca |
        | SUDIP CHAUDHARY | C0922310 | C0922310@mylambton.ca |


        - **Course**: Software Tools and Emerging Technologies for AI and ML
        - **Term**: 3rd
        - **Instructor**: [Peter Sigurdson](https://www.linkedin.com/in/petersigurdson/)

        """
    )

    with gr.Tabs():
        # Chat tab
        with gr.Tab("Chat"):
            with gr.Row():
                with gr.Column(scale=3):
                    chatbot_interface = gr.Chatbot(label="Conversation", height=400)

                    with gr.Row():
                        msg = gr.Textbox(
                            placeholder="Type your message here...",
                            label="Your message",
                            lines=2,
                            show_label=False,
                        )
                        send_btn = gr.Button("Send", variant="primary")

                    with gr.Accordion("Examples", open=False):
                        gr.Examples(
                            examples=[
                                "Hello!",
                                "How are you?",
                                "What can you help me with?",
                                "Thank you",
                                "Goodbye",
                            ],
                            inputs=msg,
                        )

                with gr.Column(scale=1):
                    gr.Markdown("### Analysis")
                    intent_label = gr.Label(label="Predicted Intent")
                    confidence_score = gr.Number(label="Confidence Score")

                    gr.Markdown("### Settings")
                    confidence_slider = gr.Slider(
                        label="Confidence Threshold",
                        minimum=0.0,
                        maximum=1.0,
                        step=0.05,
                        value=chatbot.confidence_threshold,
                    )
                    default_resp = gr.Textbox(
                        label="Default Response",
                        value=chatbot.default_response,
                        lines=2,
                    )
                    update_settings_btn = gr.Button("Update Settings")

            # Event handlers for chat
            def user_message(user_message, history):
                return "", history + [[user_message, None]]

            def bot_message(history):
                if history:
                    user_message = history[-1][0]
                    intent, response, confidence = chatbot.get_response(user_message)
                    history[-1][1] = response
                    return history, intent, confidence
                return history, "N/A", 0.0

            msg.submit(
                user_message,
                [msg, chatbot_interface],
                [msg, chatbot_interface],
                queue=False,
            ).then(
                bot_message,
                chatbot_interface,
                [chatbot_interface, intent_label, confidence_score],
            )

            send_btn.click(
                user_message,
                [msg, chatbot_interface],
                [msg, chatbot_interface],
                queue=False,
            ).then(
                bot_message,
                chatbot_interface,
                [chatbot_interface, intent_label, confidence_score],
            )

            update_settings_btn.click(
                update_settings,
                [confidence_slider, default_resp],
                gr.Textbox(label="Status"),
            )

        # Intents Management tab
        with gr.Tab("Intents Management"):
            with gr.Row():
                with gr.Column():
                    gr.Markdown("### Add New Intent")
                    new_intent_name = gr.Textbox(label="Intent Name")
                    new_patterns = gr.Textbox(label="Patterns (one per line)", lines=5)
                    new_responses = gr.Textbox(
                        label="Responses (one per line)", lines=5
                    )
                    add_intent_btn = gr.Button("Add Intent", variant="primary")
                    add_intent_status = gr.Textbox(label="Status")

                with gr.Column():
                    gr.Markdown("### Edit Intent")
                    edit_intent_dropdown = gr.Dropdown(
                        label="Select Intent to Edit",
                        choices=get_intent_list(),
                        interactive=True,
                    )
                    edit_patterns = gr.Textbox(label="Patterns (one per line)", lines=5)
                    edit_responses = gr.Textbox(
                        label="Responses (one per line)", lines=5
                    )

                    with gr.Row():
                        update_intent_btn = gr.Button("Update Intent")
                        delete_intent_btn = gr.Button("Delete Intent", variant="stop")

                    edit_intent_status = gr.Textbox(label="Status")

            with gr.Row():
                with gr.Column():
                    gr.Markdown("### Import/Export Intents")
                    with gr.Row():
                        export_intents_btn = gr.Button("Export Intents")
                        import_intents_file = gr.File(
                            label="Import Intents (JSON file)"
                        )
                    import_export_status = gr.Textbox(label="Status")

                with gr.Column():
                    gr.Markdown("### Current Intents")
                    refresh_intents_btn = gr.Button("Refresh Intents List")
                    intents_list = gr.Markdown()

            # Event handlers for intents management
            add_intent_btn.click(
                add_intent,
                [new_intent_name, new_patterns, new_responses],
                add_intent_status,
            )

            # Update dropdown when adding/deleting intents
            add_intent_btn.click(get_intent_list, [], edit_intent_dropdown)

            edit_intent_dropdown.change(
                get_intent_details,
                edit_intent_dropdown,
                [edit_patterns, edit_responses],
            )

            update_intent_btn.click(
                edit_intent,
                [edit_intent_dropdown, edit_patterns, edit_responses],
                edit_intent_status,
            )

            delete_intent_btn.click(
                delete_intent, edit_intent_dropdown, edit_intent_status
            ).then(get_intent_list, [], edit_intent_dropdown)

            export_intents_btn.click(export_intents, [], import_export_status)

            import_intents_file.change(
                import_intents_from_file, import_intents_file, import_export_status
            ).then(get_intent_list, [], edit_intent_dropdown)

            refresh_intents_btn.click(list_intents, [], intents_list)

        # Training tab
        with gr.Tab("Training"):
            with gr.Row():
                with gr.Column():
                    gr.Markdown("### Train Model")
                    epochs_input = gr.Number(
                        label="Epochs", value=500, minimum=100, maximum=5000, step=100
                    )
                    learning_rate_input = gr.Number(
                        label="Learning Rate",
                        value=0.01,
                        minimum=0.0001,
                        maximum=0.1,
                        step=0.001,
                    )
                    hidden_layers_input = gr.Textbox(
                        label="Hidden Layers (comma-separated)", value="32, 16"
                    )
                    dropout_rate_input = gr.Number(
                        label="Dropout Rate",
                        value=0.2,
                        minimum=0.0,
                        maximum=0.5,
                        step=0.05,
                    )
                    train_btn = gr.Button("Train Model", variant="primary")

                with gr.Column():
                    training_status = gr.Textbox(label="Training Status", lines=6)
                    training_plot = gr.Image(label="Training History")

            with gr.Row():
                with gr.Column():
                    gr.Markdown("### Model Management")
                    save_model_btn = gr.Button("Save Current Model")
                    load_model_file = gr.File(label="Load Model (JSON file)")
                    model_status = gr.Textbox(label="Status")

            # Event handlers for training
            train_btn.click(
                train_model,
                [
                    epochs_input,
                    learning_rate_input,
                    hidden_layers_input,
                    dropout_rate_input,
                ],
                [training_status, training_plot],
            )

            save_model_btn.click(save_model, [], model_status)

            load_model_file.change(load_model_from_file, load_model_file, model_status)

        # About tab
        with gr.Tab("About"):
            gr.Markdown(
                """
            ## Neural Network Chatbot
            
            This chatbot uses a neural network to understand and respond to user messages. 
            The model is trained on a set of intents, each with patterns and responses.
            
            ### Features:
            
            - **Neural Network Backend**: The chatbot uses a fully-connected neural network with configurable layers.
            - **Intent Recognition**: Recognizes user intents based on trained patterns.
            - **Customizable Responses**: Each intent has multiple possible responses for variety.
            - **Training Interface**: Train the model directly from the web interface.
            - **Intent Management**: Add, edit, delete, import, and export intents.
            - **Model Management**: Save and load models for future use.
            
            ### How to Use:
            
            1. **Chat Tab**: Interact with the chatbot.
            2. **Intents Management Tab**: Manage the chatbot's knowledge.
            3. **Training Tab**: Train the neural network model.
            4. **About Tab**: Learn about the chatbot and its features.
            
            ### Technical Details:
            
            - Built with Python, NumPy, and Gradio.
            - Uses a bag-of-words approach for text representation.
            - Neural network with configurable hidden layers and activation functions.
            - Cross-entropy loss for multi-class classification.
            
            Created for deployment on Hugging Face Spaces.
            """
            )

# Call initialize again after defining the UI
# to make sure dropdown is populated
chat_intents = get_intent_list()

# Launch the app
if __name__ == "__main__":
    demo.launch()