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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.") |