import sys
import os

# Get the absolute path of the directory where this script is located
current_dir = os.path.dirname(os.path.abspath(__file__))

# If the script is in /src, add that directory to sys.path so it can find flc_core.py
if current_dir not in sys.path:
    sys.path.insert(0, current_dir)

# Now you can safely import your module
from flc_core import flc_encode_file, flc_decode_file, cosine_similarity_bytes

import streamlit as st
import os
import tempfile
import numpy as np
from PIL import Image
from flc_core import flc_encode_file, flc_decode_file, cosine_similarity_bytes

st.set_page_config(page_title="FLC v1.3 | How it Works", layout="wide")

# Styling for a "Scientific Laboratory" look
st.markdown("""
    <style>
    .reportview-container { background: #0e1117; }
    .main { color: #e0e0e0; }
    h1, h2, h3 { color: #f1c40f !important; }
    .stAlert { background-color: #1a1c24; border: 1px solid #f1c40f; }
    </style>
    """, unsafe_allow_html=True)

st.title("🌀 Fibonacci Lattice Compression (FLC)")
st.markdown("""
    **FLC v1.3** is a bio-inspired data compression architecture. Unlike standard ZIP or JPEG formats, 
    FLC uses the **Golden Ratio ($\Phi$)** to decide which parts of your data are "essential" and which are "noise."
""")

# --- PILLAR 1: THE EXPLAINER ---
with st.expander("📖 Step-by-Step: How does the 'Secret Sauce' work?"):
    col1, col2, col3 = st.columns(3)
    
    with col1:
        st.markdown("### 1. Spectral Projection")
        st.write("""
            We treat your data like a sound wave. Using a **DCT (Discrete Cosine Transform)**, 
            we project the bits into frequency space. 
            * **Low Frequencies:** The "skeleton" of your data.
            * **High Frequencies:** The "dust" and fine details.
        """)

    with col2:
        st.markdown("### 2. The Golden Filter")
        st.write("""
            Instead of treating all frequencies equally, FLC uses **Fibonacci Bands**. 
            We compress the 'dust' using steps based on the **Golden Ratio ($\Phi \approx 1.618$)**. 
            As the frequency increases, the compression gets exponentially more aggressive.
        """)

    with col3:
        with st.container():
            st.markdown("### 3. Fibonacci Coding")
            st.write("""
                Standard computers use 8-bit bytes. FLC uses **Fibonacci Binary**. 
                It's a "universal code" that uses the sum of Fibonacci numbers to represent values, 
                making the compressed stream incredibly resilient and dense.
            """)

st.divider()

# --- PILLAR 2: THE INTERACTIVE DEMO ---
st.header("🧪 Test the Horizon")
with st.sidebar:
    st.header("🎛️ Architecture Params")
    st.info("Adjusting these changes how the 'Secret Sauce' math is applied.")
    
    quality_map = {
        "High Compression (Lossy)": {"bands": 6, "step": 0.08, "desc": "Aggressive $\Phi$-scaling."},
        "Balanced": {"bands": 12, "step": 0.005, "desc": "The Golden Mean of fidelity."},
        "Near-Lossless": {"bands": 24, "step": 0.0001, "desc": "Full spectral recovery."}
    }
    
    tier = st.radio("Fidelity Tier", list(quality_map.keys()), index=1)
    st.caption(quality_map[tier]["desc"])
    
    st.subheader("Visual Overlays")
    show_spiral = st.checkbox("Fibonacci Spiral Outlines", value=True)
    show_ring = st.checkbox("Event Horizon Ring", value=True)

uploaded_file = st.file_uploader("Upload a file (Image, Text, or Binary)", type=["bin", "png", "jpg", "txt"])

if uploaded_file is not None:
    with tempfile.TemporaryDirectory() as tmpdir:
        in_path = os.path.join(tmpdir, "input.bin")
        out_flc = os.path.join(tmpdir, "output.flc")
        out_gif = os.path.join(tmpdir, "unzip.gif")
        recovered_path = os.path.join(tmpdir, "recovered.bin")
        
        with open(in_path, "wb") as f:
            f.write(uploaded_file.getbuffer())
            
        if st.button("RUN HOLOGRAPHIC RECONSTRUCTION"):
            with st.status("Initializing Fibonacci Manifolds...", expanded=True) as status:
                st.write("Transforming data to Frequency Space...")
                enc = flc_encode_file(
                    in_path, out_flc, unzip_gif=out_gif,
                    n_bands=quality_map[tier]["bands"], 
                    base_step=quality_map[tier]["step"]
                )
                
                st.write("Applying $\Phi$-scaled quantization...")
                dec = flc_decode_file(out_flc, recovered_path)
                
                st.write("Generating Holographic Unzip visualization...")
                status.update(label="Reconstruction Complete!", state="complete", expanded=False)

            # Results Section
            st.subheader("📊 Compression Performance")
            c1, c2, c3, c4 = st.columns(4)
            c1.metric("Original Size", f"{enc['n_bytes']} B")
            c2.metric("Compressed Size", f"{enc['payload_len']} B")
            c3.metric("Ratio", f"{enc['ratio']:.2%}")
            
            # Calculate Similarity
            orig_data = open(in_path, "rb").read()
            reco_data = open(recovered_path, "rb").read()
            fidelity = cosine_similarity_bytes(orig_data, reco_data)
            c4.metric("Data Fidelity", f"{fidelity*100:.2f}%")

            st.divider()

            # Visualization
            st.header("🎞️ The Unzip Sequence")
            st.markdown("""
                This animation shows the **Progressive Reconstruction**. 
                The 'Hologram' on the left shows the frequency data being added band-by-band. 
                The 'Spiral' on the right shows the bits filling the Fibonacci tiles in real-time.
            """)
            
            if os.path.exists(out_gif):
                st.image(out_gif, use_container_width=True)
            
            st.info("💡 Notice how the general shape appears first, and the fine details (noise) appear last. This is the hallmark of Spectral Compression.")

            with open(out_flc, "rb") as f:
                st.download_button("📥 Download Encoded .FLC File", f, file_name="demo.flc")

else:
    st.warning("Please upload a file to visualize the Fibonacci transformation.")