import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.impute import SimpleImputer
from imblearn.over_sampling import SMOTE
from sklearn.metrics import accuracy_score, classification_report, mean_squared_error, mean_absolute_error, r2_score

# Import ML Models
from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
from sklearn.svm import SVC, SVR
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.linear_model import Perceptron

# Sidebar UI
st.sidebar.title("AI Code Generator 🧠")
st.sidebar.markdown("Generate AI models instantly!")

# Model Selection
model_options = ["KNN", "SVM", "Random Forest", "Decision Tree", "Perceptron"]
model = st.sidebar.selectbox("Choose a Model:", model_options)

# Task Selection
task_options = ["Classification", "Regression"]
task = st.sidebar.selectbox("Choose a Task:", task_options)

# Problem Selection based on Task and Model
problems = {
    "Classification": {
        "KNN": ["Spam Detection", "Disease Prediction"],
        "SVM": ["Image Recognition", "Text Classification"],
        "Random Forest": ["Fraud Detection", "Customer Segmentation"],
        "Decision Tree": ["Loan Approval", "Churn Prediction"],
        "Perceptron": ["Handwritten Digit Recognition", "Sentiment Analysis"]
    },
    "Regression": {
        "KNN": ["House Price Prediction", "Stock Prediction"],
        "SVM": ["Sales Forecasting", "Stock Market Trends"],
        "Random Forest": ["Energy Consumption", "Patient Survival Prediction"],
        "Decision Tree": ["House Price Estimation", "Revenue Prediction"],
        "Perceptron": ["Weather Forecasting", "Traffic Flow Prediction"]
    }
}

problem = st.sidebar.selectbox("Choose a Problem:", problems[task][model])

dataset_mapping = {name: f"datasets/{name.lower().replace(' ', '_')}.csv" for sublist in problems.values() for model in sublist for name in sublist[model]}

# # Dataset Selection (User selects a pre-existing fake dataset)
# dataset_mapping = {
#     "Spam Detection": "datasets/spam_detection.csv",
#     "Disease Prediction": "datasets/disease_prediction.csv",
#     "Image Recognition": "datasets/image_recognition.csv",
#     "Text Classification": "datasets/text_classification.csv",
#     "Fraud Detection": "datasets/fraud_detection.csv",
#     "Customer Segmentation": "datasets/customer_segmentation.csv",
#     "Loan Approval": "datasets/loan_approval.csv",
#     "House Price Prediction": "datasets/house_price_prediction.csv",
#     "Sales Forecasting": "datasets/sales_forecasting.csv",
# }

dataset_path = dataset_mapping.get(problem, "datasets/spam_detection.csv")
df = pd.read_csv(dataset_path)

# Display dataset
st.subheader("Sample Dataset")
st.write(df.head())

# Preprocessing Steps
st.subheader("📌 Preprocessing Steps")
st.markdown("""
- ✅ Handle Missing Values  
- ✅ Encoding Categorical Variables  
- ✅ Feature Scaling  
- ✅ Feature Selection  
- ✅ Handling Imbalanced Data using **SMOTE**
""")

# Handle missing values
imputer = SimpleImputer(strategy='mean')
df = df.apply(lambda col: imputer.fit_transform(col.values.reshape(-1, 1)).flatten() if col.dtypes == 'float64' else col)

# Encoding categorical variables
label_encoders = {}
for col in df.select_dtypes(include=['object']).columns:
    label_encoders[col] = LabelEncoder()
    df[col] = label_encoders[col].fit_transform(df[col])

# Split Data
X = df.iloc[:, :-1]  # Features
y = df.iloc[:, -1]   # Target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Feature Scaling
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Feature Selection
selector = SelectKBest(score_func=f_classif, k=min(5, X.shape[1]))  # Ensure k does not exceed available features
X_train = selector.fit_transform(X_train, y_train)
X_test = selector.transform(X_test)

# Handle imbalanced data
if task == "Classification":
    smote = SMOTE()
    X_train, y_train = smote.fit_resample(X_train, y_train)

# Model Initialization
if task == "Classification":
    n_neighbors = min(5, len(y_train))  # Ensure k is valid
    model_mapping = {
        "KNN": KNeighborsClassifier(n_neighbors=n_neighbors),
        "SVM": SVC(),
        "Random Forest": RandomForestClassifier(),
        "Decision Tree": DecisionTreeClassifier(),
        "Perceptron": Perceptron()
    }
else:
    n_neighbors = min(5, len(y_train))  # Ensure k is valid
    model_mapping = {
        "KNN": KNeighborsRegressor(n_neighbors=n_neighbors),
        "SVM": SVR(),
        "Random Forest": RandomForestRegressor(),
        "Decision Tree": DecisionTreeRegressor(),
        "Perceptron": Perceptron()
    }

model_instance = model_mapping[model]

# Train Model
model_instance.fit(X_train, y_train)
y_pred = model_instance.predict(X_test)

# Model Evaluation
st.subheader("📊 Model Evaluation")

if task == "Classification":
    accuracy = accuracy_score(y_test, y_pred)
    report = classification_report(y_test, y_pred, output_dict=True)

    st.write(f"**Accuracy:** {accuracy:.2f}")
    st.json(report)  # Shows detailed structured metrics
    
elif task == "Regression":
    mse = mean_squared_error(y_test, y_pred)
    mae = mean_absolute_error(y_test, y_pred)
    r2 = r2_score(y_test, y_pred)
    
    st.write(f"**Mean Squared Error (MSE):** {mse:.4f}")
    st.write(f"**Mean Absolute Error (MAE):** {mae:.4f}")
    st.write(f"**R² Score:** {r2:.4f}")

# Data Visualization
st.subheader("📈 Data Visualization")

# Heatmap
st.write("### 🔥 Feature Correlation")
plt.figure(figsize=(8, 5))
sns.heatmap(df.corr(), annot=True, cmap="coolwarm")
st.pyplot(plt)

# Pair Plot
st.write("### 📊 Pair Plot of Features")
sns.pairplot(df, diag_kind='kde')
st.pyplot()

# Feature Importance (for tree-based models)
if model in ["Random Forest", "Decision Tree"]:
    feature_importances = model_instance.feature_importances_
    feature_names = X.columns
    importance_df = pd.DataFrame({"Feature": feature_names, "Importance": feature_importances})
    importance_df = importance_df.sort_values(by="Importance", ascending=False)
    
    st.write("### 🌟 Feature Importance")
    fig, ax = plt.subplots()
    sns.barplot(x=importance_df["Importance"], y=importance_df["Feature"], ax=ax)
    st.pyplot(fig)

# Download Code
st.download_button("🐍 Download Python Code (.py)", "ai_model.py")
st.download_button("📓 Download Notebook (.ipynb)", "ai_model.ipynb")
st.markdown("[🚀 Open in Colab](https://colab.research.google.com/)")

st.success("Code generated! Download and do magic! ✨")