import streamlit as st
import sys
import os
import json
from pathlib import Path
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

sys.path.append(os.path.dirname(os.path.dirname(__file__)))

PROCESSED_DIR = "data/processed_results"


def apply_material_css():
    st.markdown("""
        <style>
        /* Material Design CSS Overrides */

        .stApp {
            font-family: 'Roboto', 'Inter', sans-serif;
            background-color: #121212;
            color: #FFFFFF;
        }

        /* Material Cards for metrics and sections */
        .material-card {
            background-color: #1E1E1E;
            border-radius: 8px;
            padding: 20px;
            box-shadow: 0 4px 6px rgba(0,0,0,0.3);
            margin-bottom: 20px;
        }

        h1, h2, h3, h4 {
            font-weight: 500;
        }

        /* Subtle styling for Streamlit columns to look like cards */
        [data-testid="column"] {
            background-color: #1E1E1E;
            border-radius: 8px;
            padding: 20px;
            box-shadow: 0 4px 6px rgba(0,0,0,0.3);
        }

        /* Expander (Transcript) - keep dark theme even on hover/focus */
        div[data-testid="stExpander"] details,
        div[data-testid="stExpander"] summary,
        div[data-testid="stExpander"] summary:hover,
        div[data-testid="stExpander"] summary:focus,
        div[data-testid="stExpander"] summary:active,
        div[data-testid="stExpander"] summary:focus-visible {
            background-color: #1E1E1E !important;
            color: #FFFFFF !important;
        }

        div[data-testid="stExpander"] details {
            border: 1px solid #333333 !important;
            border-radius: 8px !important;
        }

        /* Transcript text area */
        div[data-testid="stExpander"] textarea,
        div[data-testid="stExpander"] textarea:hover,
        div[data-testid="stExpander"] textarea:focus,
        div[data-testid="stExpander"] textarea:active {
            background-color: #121212 !important;
            color: #FFFFFF !important;
            border-color: #333333 !important;
        }

        </style>
    """, unsafe_allow_html=True)


def display_results(final_state):
    st.subheader("Workflow Results")

    metadata = final_state.get("metadata", {})
    if isinstance(metadata, dict) and metadata.get("intake_error"):
        st.error(f"CSV validation failed: {metadata['intake_error']}")
        st.info("This CSV was not accepted for processing. Please fix the headers and re-upload.")
        st.markdown("### Metadata")
        for k, v in metadata.items():
            st.markdown(f"- **{k.replace('_', ' ').title()}**: {v}")
        return

    st.markdown("### Transcript")
    transcript_text = final_state.get("clean_content") or final_state.get("content") or ""
    if transcript_text:
        with st.expander("View transcript", expanded=False):
            st.text_area("Transcript", transcript_text, height=220)
    else:
        st.write("No transcript available.")

    col1, col2 = st.columns(2)
    with col1:
        st.markdown("### Summary")
        st.write(final_state.get("summary", "No summary generated."))

        st.markdown("### Key Points")
        key_points = final_state.get("key_points", "No key points generated.")
        if isinstance(key_points, list):
            for point in key_points:
                st.markdown(f"- {point}")
        else:
            st.write(key_points)

        st.markdown("### Action Items")
        action_items = final_state.get("action_items", "No action items generated.")
        if isinstance(action_items, list):
            if action_items:
                for item in action_items:
                    st.markdown(f"- {item}")
            else:
                st.write("No action items generated.")
        else:
            st.write(action_items)

        st.markdown("### Tags / Highlights")
        tags = final_state.get("tags") or []
        highlights = final_state.get("highlights") or []
        if tags:
            st.write("**Tags:** " + ", ".join([str(t) for t in tags]))
        else:
            st.write("**Tags:** None")
        if highlights:
            st.write("**Highlights:**")
            for h in highlights:
                st.markdown(f"- {h}")
        else:
            st.write("**Highlights:** None")

    with col2:
        st.markdown("### Scoring Rubric")
        quality_scores = final_state.get("quality_scores", {})
        if isinstance(quality_scores, dict) and quality_scores:
            import plotly.graph_objects as go
            for metric in ['tone', 'professionalism', 'structured_resolution']:
                if metric in quality_scores:
                    try:
                        val = float(quality_scores[metric])
                        fig = go.Figure(go.Indicator(
                            mode="gauge+number",
                            value=val,
                            title={'text': metric.replace('_', ' ').title(), 'font': {'size': 16, 'color': '#FFFFFF'}},
                            gauge={
                                'axis': {'range': [None, 10], 'tickwidth': 1, 'tickcolor': "#BB86FC"},
                                'bar': {'color': "#BB86FC"},
                                'bgcolor': "#1E1E1E",
                                'borderwidth': 2,
                                'bordercolor': "#333333",
                                'steps': [
                                    {'range': [0, 4], 'color': '#cf6679'},
                                    {'range': [4, 7], 'color': '#ffb74d'},
                                    {'range': [7, 10], 'color': '#81c784'}],
                            }
                        ))
                        # Adjust colors for dark theme
                        fig.update_layout(
                            height=180,
                            margin=dict(l=20, r=20, t=40, b=20),
                            paper_bgcolor='#1E1E1E',
                            plot_bgcolor='#1E1E1E',
                            font={'color': '#FFFFFF'}
                        )
                        st.plotly_chart(fig, width="stretch")
                    except (ValueError, TypeError):
                        st.write(f"**{metric.replace('_', ' ').title()}**: {quality_scores[metric]}")

            if "notes" in quality_scores:
                st.write("**Notes:**", quality_scores["notes"])

            if "rubric" in quality_scores and quality_scores["rubric"]:
                with st.expander("View scoring rubric", expanded=False):
                    rubric_rows = [
                        {
                            "Dimension": "Tone",
                            "0": "Hostile/arguing",
                            "3": "Curt/tense",
                            "5": "Neutral",
                            "7": "Friendly/empathic",
                            "10": "Consistently calm, respectful, de-escalating",
                        },
                        {
                            "Dimension": "Professionalism",
                            "0": "Rude/unprofessional",
                            "3": "Unclear or dismissive",
                            "5": "Acceptable",
                            "7": "Clear, courteous, policy-aligned",
                            "10": "Excellent clarity, appropriate boundaries, ownership",
                        },
                        {
                            "Dimension": "Structured resolution",
                            "0": "No attempt",
                            "3": "Vague / no next steps",
                            "5": "Partial (some questions/steps)",
                            "7": "Clear diagnosis + next steps + confirmation",
                            "10": "Fully structured (issue, actions, timelines, confirmation, closure)",
                        },
                    ]

                    st.dataframe(
                        rubric_rows,
                        hide_index=True,
                        use_container_width=True,
                    )
                    st.caption(
                        "Notes must cite 1–3 specific behaviors from the transcript (avoid long quotes)."
                    )
        else:
            st.write("No scoring rubric results generated.", quality_scores)

        st.markdown("### Metadata")
        if isinstance(metadata, dict) and metadata:
            for k, v in metadata.items():
                st.markdown(f"- **{k.replace('_', ' ').title()}**: {v}")
        else:
            st.write("No metadata available.")


def main():
    st.set_page_config(page_title="Call Center Data Analysis", layout="wide")
    apply_material_css()

    st.title("Call Center Data Analysis Dashboard")

    os.makedirs(PROCESSED_DIR, exist_ok=True)
    os.makedirs("tmp", exist_ok=True)

    if "view_mode" not in st.session_state:
        st.session_state.view_mode = "none"

    def set_upload_mode():
        st.session_state.view_mode = "upload"

    def set_dropdown_mode():
        st.session_state.view_mode = "dropdown"

    st.sidebar.header("Upload New File")
    uploaded_file = st.sidebar.file_uploader(
        "Upload a file",
        type=["mp3", "wav", "csv", "json"],
        on_change=set_upload_mode
    )

    st.sidebar.markdown("---")
    st.sidebar.header("Processed Files")

    # Get list of processed files
    processed_files = [f for f in os.listdir(PROCESSED_DIR) if f.endswith(".json")]
    processed_files.sort(reverse=True)  # Show newest (or reverse alphabetical) first

    selected_file = None
    if processed_files:
        options = ["-- Select a file --"] + processed_files
        selected_dropdown = st.sidebar.selectbox(
            "View cached results:",
            options,
            format_func=lambda x: x.replace(".json", "") if x != "-- Select a file --" else x,
            on_change=set_dropdown_mode
        )
        if selected_dropdown != "-- Select a file --":
            selected_file = selected_dropdown
    else:
        st.sidebar.info("No files processed yet.")

    st.sidebar.markdown("---")
    st.sidebar.header("Notes")
    st.sidebar.markdown("""
    Sample data:
    - [Customer Call Center Dataset Analysis](https://www.kaggle.com/datasets/rafaqatkhan608/customer-call-center-dataset-analysis/code/data)
    - [E-commerce Customer Support English Audio](https://huggingface.co/datasets/HumynLabs/e-commerce-customersupport-english-audio/tree/main)
    """)

    # Prioritize based on view_mode
    if st.session_state.view_mode == "upload" and uploaded_file is not None:
        st.success(f"File '{uploaded_file.name}' uploaded successfully!")

        from src.workflow import build_workflow

        file_path = os.path.join("tmp", uploaded_file.name)
        with open(file_path, "wb") as f:
            f.write(uploaded_file.getbuffer())

        file_extension = uploaded_file.name.split('.')[-1].lower()

        # Check if already processed to avoid reprocessing on rerun if same file is in uploader
        cached_json_path = os.path.join(PROCESSED_DIR, f"{uploaded_file.name}.json")

        if os.path.exists(cached_json_path):
            st.info("Loading cached results for this file...")
            with open(cached_json_path, 'r') as f:
                final_state = json.load(f)
            display_results(final_state)
        else:
            st.write("Processing file through LangGraph Workflow...")
            with st.spinner("Agents are analyzing the data..."):
                workflow = build_workflow()
                initial_state = {
                    "file_path": file_path,
                    "file_type": file_extension
                }

                thread_id = Path(file_path).name
                final_state = workflow.invoke(
                    initial_state, config={"configurable": {"thread_id": thread_id}}
                )

                if final_state.get("metadata", {}).get("intake_error"):
                    display_results(final_state)
                else:
                    # Cache the results
                    with open(cached_json_path, 'w') as f:
                        json.dump(final_state, f)
                    st.rerun()

            if not final_state.get("metadata", {}).get("intake_error"):
                display_results(final_state)

    elif (
            st.session_state.view_mode == "dropdown" or st.session_state.view_mode == "none") and selected_file is not None:
        st.info(f"Loading cached results for '{selected_file.replace('.json', '')}'")
        cached_json_path = os.path.join(PROCESSED_DIR, selected_file)
        with open(cached_json_path, 'r') as f:
            final_state = json.load(f)
        display_results(final_state)

    elif st.session_state.view_mode != "dropdown" and uploaded_file is not None:
        st.success(f"File '{uploaded_file.name}' uploaded successfully!")

        from src.workflow import build_workflow

        file_path = os.path.join("tmp", uploaded_file.name)
        with open(file_path, "wb") as f:
            f.write(uploaded_file.getbuffer())

        file_extension = uploaded_file.name.split('.')[-1].lower()

        cached_json_path = os.path.join(PROCESSED_DIR, f"{uploaded_file.name}.json")

        if os.path.exists(cached_json_path):
            st.info("Loading cached results for this file...")
            with open(cached_json_path, 'r') as f:
                final_state = json.load(f)
            display_results(final_state)
        else:
            st.write("Processing file through LangGraph Workflow...")
            with st.spinner("Agents are analyzing the data..."):
                workflow = build_workflow()
                initial_state = {
                    "file_path": file_path,
                    "file_type": file_extension
                }
                thread_id = Path(file_path).name
                final_state = workflow.invoke(
                    initial_state, config={"configurable": {"thread_id": thread_id}}
                )
                if final_state.get("metadata", {}).get("intake_error"):
                    display_results(final_state)
                else:
                    with open(cached_json_path, 'w') as f:
                        json.dump(final_state, f)
                    st.rerun()
            if not final_state.get("metadata", {}).get("intake_error"):
                display_results(final_state)

    else:
        st.info("Please upload a file or select a previously processed file.")


if __name__ == "__main__":
    main()