import streamlit as st
import pandas as pd
from pandas_profiling import ProfileReport
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
import plotly.express as px

# Data Ingestion
uploaded_file = st.file_uploader("Upload your dataset:", type=["csv", "xlsx", "json"])
if uploaded_file:
    df = pd.read_csv(uploaded_file)
    st.write("Data Preview:")
    st.write(df.head())

# Data Preparation
st.write("Data Preparation:")
profile = ProfileReport(df, title="Data Profiling Report")
st.write(profile)

# Data Cleaning
st.write("Data Cleaning:")
handle_missing_values = st.selectbox("Handle missing values:", ["Mean",_"Median",_"Imputation"])
handle_outliers = st.selectbox("Handle outliers:", ["Standardization",_"_Winsorization"])

# Model Training
st.write("Model Training:")
model_type = st.selectbox("Choose a model:", ["Linear_Regression",_"Decision_Trees",_"Random_Forest"])
hyperparams = {}
if model_type == "Linear Regression":
    hyperparams["alpha"] = st.slider("Regularization strength:", 0.1, 10.0)
elif model_type == "Decision Trees":
    hyperparams["max_depth"] = st.slider("Maximum depth:", 1, 10)
elif model_type == "Random Forest":
    hyperparams["n_estimators"] = st.slider("Number of estimators:", 10, 100)

X_train, X_test, y_train, y_test = train_test_split(df.drop("target", axis=1), df["target"], test_size=0.2, random_state=42)

if model_type == "Linear Regression":
    model = LinearRegression(**hyperparams)
elif model_type == "Decision Trees":
    model = DecisionTreeRegressor(**hyperparams)
elif model_type == "Random Forest":
    model = RandomForestRegressor(**hyperparams)

model.fit(X_train, y_train)
y_pred = model.predict(X_test)

# Model Evaluation
st.write("Model Evaluation:")
st.write("Accuracy:", model.score(X_test, y_test))
st.write("Confusion Matrix:")
conf_mat = pd.crosstab(y_test, y_pred, rownames=["Actual"], colnames=["Predicted"])
st.plotly(px.imshow(conf_mat, color_continuous_scale="blues"), use_container_width=True)

# Model Deployment
st.write("Model Deployment:")
download_model = st.download_button("Download trained model", data=model, file_name="model.py")
deploy_to_cloud = st.button("Deploy to cloud platform")