import gradio as gr
import pandas as pd
import numpy as np
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, accuracy_score
import torch
from torch import nn
from torch.autograd import Variable

# GAN-based anomaly detection for financial analysis
class GANRiskAnalyzer:
    def __init__(self, input_dim, hidden_dim, output_dim):
        self.generator = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, output_dim),
            nn.Tanh()
        )
        self.discriminator = nn.Sequential(
            nn.Linear(output_dim, hidden_dim),
            nn.LeakyReLU(0.2),
            nn.Linear(hidden_dim, 1),
            nn.Sigmoid()
        )
        self.loss = nn.BCELoss()
        self.generator_optimizer = torch.optim.Adam(self.generator.parameters(), lr=0.0002)
        self.discriminator_optimizer = torch.optim.Adam(self.discriminator.parameters(), lr=0.0002)

    def train(self, data, epochs=100):
        real_labels = Variable(torch.ones(data.size(0), 1))
        fake_labels = Variable(torch.zeros(data.size(0), 1))
        for epoch in range(epochs):
            # Train Discriminator
            self.discriminator_optimizer.zero_grad()
            real_data = Variable(data)
            real_output = self.discriminator(real_data)
            real_loss = self.loss(real_output, real_labels)

            z = Variable(torch.randn(data.size(0), data.size(1)))
            fake_data = self.generator(z)
            fake_output = self.discriminator(fake_data.detach())
            fake_loss = self.loss(fake_output, fake_labels)

            d_loss = real_loss + fake_loss
            d_loss.backward()
            self.discriminator_optimizer.step()

            # Train Generator
            self.generator_optimizer.zero_grad()
            fake_output = self.discriminator(fake_data)
            g_loss = self.loss(fake_output, real_labels)
            g_loss.backward()
            self.generator_optimizer.step()

    def generate(self, n_samples, input_dim):
        z = Variable(torch.randn(n_samples, input_dim))
        generated_data = self.generator(z)
        return generated_data.detach().numpy()

# Risk Analysis
def analyze_financial_data(file):
    # Load CSV data
    data = pd.read_csv(file)
    
    # Check required columns
    required_columns = ["Revenue", "Profit", "Loss", "Expenses", "Risk_Level"]
    if not all(column in data.columns for column in required_columns):
        return "The uploaded CSV must include these columns: Revenue, Profit, Loss, Expenses, Risk_Level."

    # Data Preprocessing
    X = data[["Revenue", "Profit", "Loss", "Expenses"]]
    y = data["Risk_Level"]
    
    scaler = StandardScaler()
    X_scaled = scaler.fit_transform(X)
    
    # Dimensionality Reduction
    pca = PCA(n_components=2)
    X_pca = pca.fit_transform(X_scaled)
    
    # Train-Test Split
    X_train, X_test, y_train, y_test = train_test_split(X_pca, y, test_size=0.2, random_state=42)
    
    # Gradient Boosting Classifier
    model = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, max_depth=5)
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    
    accuracy = accuracy_score(y_test, y_pred)
    report = classification_report(y_test, y_pred, output_dict=True)

    # GAN-based Anomaly Detection
    gan = GANRiskAnalyzer(input_dim=X_pca.shape[1], hidden_dim=128, output_dim=X_pca.shape[1])
    gan.train(torch.tensor(X_pca, dtype=torch.float32), epochs=200)
    anomalies = gan.generate(n_samples=5, input_dim=X_pca.shape[1])

    # Analysis Insights
    total_revenue = data["Revenue"].sum()
    total_profit = data["Profit"].sum()
    total_loss = data["Loss"].sum()

    insights = {
        "Accuracy": f"{accuracy * 100:.2f}%",
        "Classification Report": report,
        "Generated Anomalies (GAN)": anomalies.tolist(),
        "Financial Summary": {
            "Total Revenue": f"${total_revenue:,.2f}",
            "Total Profit": f"${total_profit:,.2f}",
            "Total Loss": f"${total_loss:,.2f}",
            "Net Balance": f"${(total_revenue - total_loss):,.2f}"
        }
    }
    return insights

with gr.Blocks(theme=gr.themes.Monochrome()) as interface:
    gr.Markdown("# **AI Risk Analyst Agent**")
    gr.Markdown(
        "Analyze your financial risks and identify anomalies using advanced AI models. Upload financial data in CSV format to get started."
    )
    
    with gr.Row():
        with gr.Column():
            data_file = gr.File(label="Upload Financial Data (CSV)")
            submit_button = gr.Button("Analyze")
        with gr.Column():
            output = gr.JSON(label="Risk Analysis Insights")

    submit_button.click(analyze_financial_data, inputs=[data_file], outputs=output)

interface.launch()