import streamlit as st
import pandas as pd
import io
from utils import query_agent, detect_delimiter
from dotenv import load_dotenv
import os

# Set Streamlit page configuration
st.set_page_config(
    page_title="HF Data Analyst Agent",
    page_icon="📊",
    layout="centered"
)

# Load environment variables
load_dotenv()
# Retrieve API Token from .env or environment variables
HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")

# --- Custom Styling (Tailwind-like aesthetic using Streamlit's style) ---
st.markdown("""
<style>
.main-header {
    font-size: 2.5em;
    font-weight: 700;
    color: #4B0082; /* Deep Purple */
    text-align: center;
    margin-bottom: 0.5em;
}
.stButton>button {
    background-color: #6A5ACD; /* Slate Blue */
    color: white;
    border-radius: 12px;
    border: 2px solid #483D8B;
    padding: 10px 20px;
    font-size: 16px;
    transition: all 0.3s ease;
}
.stButton>button:hover {
    background-color: #483D8B; /* Darker Blue */
    color: #E0FFFF;
    box-shadow: 0 4px 12px rgba(0, 0, 0, 0.2);
}
.uploaded-file-details {
    margin-top: 15px;
    padding: 15px;
    border: 1px solid #DDA0DD; /* Plum */
    border-radius: 10px;
    background-color: #F8F8FF; /* Ghost White */
}
.analysis-input-box {
    margin-top: 20px;
    padding: 20px;
    border: 2px solid #7B68EE; /* Medium Slate Blue */
    border-radius: 15px;
    background-color: #E6E6FA; /* Lavender */
}
</style>
""", unsafe_allow_html=True)


# --- Application Title and Setup ---

st.markdown('<div class="main-header">📊 AI Data Analyst powered by Mistral (HF)</div>', unsafe_allow_html=True)
st.markdown(
    """
    Upload a CSV file and ask natural language questions about your data. 
    This application uses the **Mistral-7B-Instruct-v0.2** model via the Hugging Face Inference Endpoint for fast, accurate analysis.
    """,
    unsafe_allow_html=True
)

if not HUGGINGFACEHUB_API_TOKEN or HUGGINGFACEHUB_API_TOKEN == "YOUR_HUGGINGFACE_TOKEN_HERE":
    st.error("⚠️ **API Token Missing!** Please set your `HUGGINGFACEHUB_API_TOKEN` in the `.env` file or as a secret in your Hugging Face Space.")
else:
    # --- File Uploader ---
    uploaded_file = st.file_uploader(
        "Upload your CSV file", 
        type=["csv"],
        accept_multiple_files=False,
        help="Upload a CSV file. The app automatically detects delimiters (like comma, semicolon, etc.)."
    )

    if uploaded_file is not None:
        # Read the file content into bytes
        file_content_bytes = uploaded_file.getvalue()

        st.markdown('<div class="uploaded-file-details">', unsafe_allow_html=True)
        st.subheader("Data Preview (First 5 Rows)")
        
        try:
            delimiter = detect_delimiter(file_content_bytes)
            
            # Read the DataFrame just for display
            data_io = io.StringIO(file_content_bytes.decode('utf-8'))
            df_preview = pd.read_csv(data_io, sep=delimiter)
            
            st.dataframe(df_preview.head())
            st.write(f"Detected Delimiter: `{delimiter}`")
            st.write(f"Shape: {df_preview.shape[0]} rows, {df_preview.shape[1]} columns")

            # --- Analysis Input Box ---
            st.markdown('</div>', unsafe_allow_html=True)
            st.markdown('<div class="analysis-input-box">', unsafe_allow_html=True)
            
            st.subheader("Ask a question about the data")
            query = st.text_input(
                "Enter your question here:",
                placeholder="e.g., What is the average value of the 'Age' column?",
                key="user_query"
            )

            # --- Analysis Execution ---
            if st.button("Run Analysis", key="run_analysis_button") and query:
                with st.spinner("Analyzing data... The Mistral Agent is working hard!"):
                    # Pass the Hugging Face API token to the agent function
                    response = query_agent(file_content_bytes, query, HUGGINGFACEHUB_API_TOKEN)
                    
                    st.success("Analysis Complete!")
                    st.markdown("---")
                    st.subheader("Agent Response")
                    st.markdown(f"**{response}**")
            
            elif st.button("Run Analysis", key="run_analysis_button_disabled") and not query:
                st.warning("Please enter a question to analyze the data.")

            st.markdown('</div>', unsafe_allow_html=True)

        except Exception as e:
            st.error(f"Could not process the uploaded file or generate preview. Error: {e}")