Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,136 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 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")
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+else:
+    st.warning("Please upload a file to visualize the Fibonacci transformation.")