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import os
import torch
import streamlit as st
import hashlib
import io
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
from typing import Union
import time
from config import MODEL_CONFIG, TARGET_LEN, LABEL_MAP
from modules.callbacks import (
on_model_change,
on_input_mode_change,
on_sample_change,
reset_results,
reset_ephemeral_state,
log_message,
)
from core_logic import (
get_sample_files,
load_model,
run_inference,
parse_spectrum_data,
label_file,
)
from utils.results_manager import ResultsManager
from utils.confidence import calculate_softmax_confidence
from utils.multifile import process_multiple_files, display_batch_results
from utils.preprocessing import resample_spectrum
def load_css(file_path):
with open(file_path, encoding="utf-8") as f:
st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
@st.cache_data
def create_spectrum_plot(x_raw, y_raw, x_resampled, y_resampled, _cache_key=None):
"""Create spectrum visualization plot"""
fig, ax = plt.subplots(1, 2, figsize=(13, 5), dpi=100)
# Raw spectrum
ax[0].plot(x_raw, y_raw, label="Raw", color="dimgray", linewidth=1)
ax[0].set_title("Raw Input Spectrum")
ax[0].set_xlabel("Wavenumber (cm⁻¹)")
ax[0].set_ylabel("Intensity")
ax[0].grid(True, alpha=0.3)
ax[0].legend()
# Resampled spectrum
ax[1].plot(
x_resampled, y_resampled, label="Resampled", color="steelblue", linewidth=1
)
ax[1].set_title(f"Resampled ({len(y_resampled)} points)")
ax[1].set_xlabel("Wavenumber (cm⁻¹)")
ax[1].set_ylabel("Intensity")
ax[1].grid(True, alpha=0.3)
ax[1].legend()
fig.tight_layout()
# Convert to image
buf = io.BytesIO()
plt.savefig(buf, format="png", bbox_inches="tight", dpi=100)
buf.seek(0)
plt.close(fig) # Prevent memory leaks
return Image.open(buf)
# //////////////////////////////////////////
def render_confidence_progress(
probs: np.ndarray,
labels: list[str] = ["Stable", "Weathered"],
highlight_idx: Union[int, None] = None,
side_by_side: bool = True,
):
"""Render Streamlit native progress bars with scientific formatting."""
p = np.asarray(probs, dtype=float)
p = np.clip(p, 0.0, 1.0)
if side_by_side:
cols = st.columns(len(labels))
for i, (lbl, val, col) in enumerate(zip(labels, p, cols)):
with col:
is_highlighted = highlight_idx is not None and i == highlight_idx
label_text = f"**{lbl}**" if is_highlighted else lbl
st.markdown(f"{label_text}: {val*100:.1f}%")
st.progress(int(round(val * 100)))
else:
# Vertical layout for better readability
for i, (lbl, val) in enumerate(zip(labels, p)):
is_highlighted = highlight_idx is not None and i == highlight_idx
# Create a container for each probability
with st.container():
col1, col2 = st.columns([3, 1])
with col1:
if is_highlighted:
st.markdown(f"**{lbl}** ← Predicted")
else:
st.markdown(f"{lbl}")
with col2:
st.metric(label="", value=f"{val*100:.1f}%", delta=None)
# Progress bar with conditional styling
if is_highlighted:
st.progress(int(round(val * 100)))
st.caption("🎯 **Model Prediction**")
else:
st.progress(int(round(val * 100)))
if i < len(labels) - 1: # Add spacing between items
st.markdown("")
from typing import Optional
def render_kv_grid(d: Optional[dict] = None, ncols: int = 2):
if d is None:
d = {}
if not d:
return
items = list(d.items())
cols = st.columns(ncols)
for i, (k, v) in enumerate(items):
with cols[i % ncols]:
st.caption(f"**{k}:** {v}")
# //////////////////////////////////////////
def render_model_meta(model_choice: str):
info = MODEL_CONFIG.get(model_choice, {})
emoji = info.get("emoji", "")
desc = info.get("description", "").strip()
acc = info.get("accuracy", "-")
f1 = info.get("f1", "-")
st.caption(f"{emoji} **Model Snapshot** - {model_choice}")
cols = st.columns(2)
with cols[0]:
st.metric("Accuracy", acc)
with cols[1]:
st.metric("F1 Score", f1)
if desc:
st.caption(desc)
# //////////////////////////////////////////
def get_confidence_description(logit_margin):
"""Get human-readable confidence description"""
if logit_margin > 1000:
return "VERY HIGH", "🟢"
elif logit_margin > 250:
return "HIGH", "🟡"
elif logit_margin > 100:
return "MODERATE", "🟠"
else:
return "LOW", "🔴"
# //////////////////////////////////////////
def render_sidebar():
with st.sidebar:
# Header
st.header("AI-Driven Polymer Classification")
st.caption(
"Predict polymer degradation (Stable vs Weathered) from Raman/FTIR spectra using validated CNN models. — v0.01"
)
# Modality Selection
st.markdown("##### Spectroscopy Modality")
modality = st.selectbox(
"Choose Modality",
["raman", "ftir"],
index=0,
key="modality_select",
format_func=lambda x: f"{'Raman' if x == 'raman' else 'FTIR'}",
)
# Display modality info
if modality == "ftir":
st.info("FTIR mode: 400-4000 cm-1 range with atmospheric correction")
else:
st.info("Raman mode: 200-4000 cm-1 range with standard preprocessing")
# Model selection
st.markdown("##### AI Model Selection")
model_labels = [
f"{MODEL_CONFIG[name]['emoji']} {name}" for name in MODEL_CONFIG.keys()
]
selected_label = st.selectbox(
"Choose AI Model",
model_labels,
key="model_select",
on_change=on_model_change,
)
model_choice = selected_label.split(" ", 1)[1]
# Compact metadata directly under dropdown
render_model_meta(model_choice)
# Collapsed info to reduce clutter
with st.expander("About This App", icon=":material/info:", expanded=False):
st.markdown(
"""
**AI-Driven Polymer Aging Prediction and Classification**
**Purpose**: Classify polymer degradation using AI<br>
**Input**: Raman spectroscopy .txt files<br>
**Models**: CNN architectures for classification<br>
**Modalities**: Raman and FTIR spectroscopy support<br>
**Features**: Multi-model comparison and analysis<br>
**Contributors**<br>
- Dr. Sanmukh Kuppannagari (Mentor)<br>
- Dr. Metin Karailyan (Mentor)<br>
- Jaser Hasan (Author)<br>
**Links**<br>
[HF Space](https://huggingface.co/spaces/dev-jas/polymer-aging-ml)<br>
[GitHub Repository](https://github.com/KLab-AI3/ml-polymer-recycling)
**Citation Figure2CNN (baseline)**
Neo et al., 2023, *Resour. Conserv. Recycl.*, 188, 106718.
[https://doi.org/10.1016/j.resconrec.2022.106718](https://doi.org/10.1016/j.resconrec.2022.106718)
""",
unsafe_allow_html=True,
)
# //////////////////////////////////////////
def render_input_column():
st.markdown("##### Data Input")
mode = st.radio(
"Input mode",
["Upload File", "Batch Upload", "Sample Data"],
key="input_mode",
horizontal=True,
on_change=on_input_mode_change,
)
# == Input Mode Logic ==
if mode == "Upload File":
upload_key = st.session_state["current_upload_key"]
up = st.file_uploader(
"Upload spectrum file (.txt, .csv, .json)",
type=["txt", "csv", "json"],
help="Upload spectroscopy data: TXT (2-column), CSV (with headers), or JSON format",
key=upload_key, # ← versioned key
)
# Process change immediately
if up is not None:
raw = up.read()
text = raw.decode("utf-8") if isinstance(raw, bytes) else raw
# only reparse if its a different file|source
if (
st.session_state.get("filename") != getattr(up, "name", None)
or st.session_state.get("input_source") != "upload"
):
st.session_state["input_text"] = text
st.session_state["filename"] = getattr(up, "name", None)
st.session_state["input_source"] = "upload"
# Ensure single file mode
st.session_state["batch_mode"] = False
st.session_state["status_message"] = (
f"File '{st.session_state['filename']}' ready for analysis"
)
st.session_state["status_type"] = "success"
reset_results("New file uploaded")
# Batch Upload tab
elif mode == "Batch Upload":
st.session_state["batch_mode"] = True
# Use a versioned key to ensure the file uploader resets properly.
batch_upload_key = f"batch_upload_{st.session_state['uploader_version']}"
uploaded_files = st.file_uploader(
"Upload multiple spectrum files (.txt, .csv, .json)",
type=["txt", "csv", "json"],
accept_multiple_files=True,
help="Upload spectroscopy files in TXT, CSV, or JSON format.",
key=batch_upload_key,
)
if uploaded_files:
# Use a dictionary to keep only unique files based on name and size
unique_files = {(file.name, file.size): file for file in uploaded_files}
unique_file_list = list(unique_files.values())
num_uploaded = len(uploaded_files)
num_unique = len(unique_file_list)
# Optionally, inform the user that duplicates were removed
if num_uploaded > num_unique:
st.info(f"{num_uploaded - num_unique} duplicate file(s) were removed.")
# Use the unique list
st.session_state["batch_files"] = unique_file_list
st.session_state["status_message"] = (
f"{num_unique} ready for batch analysis"
)
st.session_state["status_type"] = "success"
else:
st.session_state["batch_files"] = []
# This check prevents resetting the status if files are already staged
if not st.session_state.get("batch_files"):
st.session_state["status_message"] = (
"No files selected for batch processing"
)
st.session_state["status_type"] = "info"
# Sample tab
elif mode == "Sample Data":
st.session_state["batch_mode"] = False
sample_files = get_sample_files()
if sample_files:
options = ["-- Select Sample --"] + [p.name for p in sample_files]
sel = st.selectbox(
"Choose sample spectrum:",
options,
key="sample_select",
on_change=on_sample_change,
)
if sel != "-- Select Sample --":
st.session_state["status_message"] = (
f"📁 Sample '{sel}' ready for analysis"
)
st.session_state["status_type"] = "success"
else:
st.info("No sample data available")
# == Status box (displays the message) ==
msg = st.session_state.get("status_message", "Ready")
typ = st.session_state.get("status_type", "info")
if typ == "success":
st.success(msg)
elif typ == "error":
st.error(msg)
else:
st.info(msg)
# Safely get model choice from session state
model_choice = st.session_state.get("model_select", " ").split(" ", 1)[1]
model = load_model(model_choice)
# Determine if the app is ready for inference
is_batch_ready = st.session_state.get("batch_mode", False) and st.session_state.get(
"batch_files"
)
is_single_ready = not st.session_state.get(
"batch_mode", False
) and st.session_state.get("input_text")
inference_ready = (is_batch_ready or is_single_ready) and model is not None
# Store for other modules to access
st.session_state["inference_ready"] = inference_ready
# Render buttons
with st.form("analysis_form", clear_on_submit=False):
submitted = st.form_submit_button(
"Run Analysis", type="primary", disabled=not inference_ready
)
st.button(
"Reset All",
on_click=reset_ephemeral_state,
help="Clear all uploaded files and results.",
)
# Handle form submission
if submitted and inference_ready:
if st.session_state.get("batch_mode"):
batch_files = st.session_state.get("batch_files", [])
with st.spinner(f"Processing {len(batch_files)} files ..."):
st.session_state["batch_results"] = process_multiple_files(
uploaded_files=batch_files,
model_choice=model_choice,
load_model_func=load_model,
run_inference_func=run_inference,
label_file_func=label_file,
)
else:
try:
x_raw, y_raw = parse_spectrum_data(st.session_state["input_text"])
x_resampled, y_resampled = resample_spectrum(x_raw, y_raw, TARGET_LEN)
st.session_state.update(
{
"x_raw": x_raw,
"y_raw": y_raw,
"x_resampled": x_resampled,
"y_resampled": y_resampled,
"inference_run_once": True,
}
)
except (ValueError, TypeError) as e:
st.error(f"Error processing spectrum data: {e}")
# //////////////////////////////////////////
def render_results_column():
# Get the current mode and check for batch results
is_batch_mode = st.session_state.get("batch_mode", False)
has_batch_results = "batch_results" in st.session_state
if is_batch_mode and has_batch_results:
# THEN render the main interactive dashboard from ResultsManager
ResultsManager.display_results_table()
elif st.session_state.get("inference_run_once", False) and not is_batch_mode:
st.markdown("##### Analysis Results")
# Get data from session state
x_raw = st.session_state.get("x_raw")
y_raw = st.session_state.get("y_raw")
x_resampled = st.session_state.get("x_resampled") # ← NEW
y_resampled = st.session_state.get("y_resampled")
filename = st.session_state.get("filename", "Unknown")
if all(v is not None for v in [x_raw, y_raw, y_resampled]):
# Run inference
if y_resampled is None:
raise ValueError(
"y_resampled is None. Ensure spectrum data is properly resampled before proceeding."
)
cache_key = hashlib.md5(
f"{y_resampled.tobytes()}{st.session_state.get('model_select', 'Unknown').split(' ', 1)[1]}".encode()
).hexdigest()
prediction, logits_list, probs, inference_time, logits = run_inference(
y_resampled,
(
st.session_state.get("model_select", "").split(" ", 1)[1]
if "model_select" in st.session_state
else None
),
_cache_key=cache_key,
)
if prediction is None:
st.error(
"❌ Inference failed: Model not loaded. Please check that weights are available."
)
st.stop() # prevents the rest of the code in this block from executing
log_message(
f"Inference completed in {inference_time:.2f}s, prediction: {prediction}"
)
# Get ground truth
true_label_idx = label_file(filename)
true_label_str = (
LABEL_MAP.get(true_label_idx, "Unknown")
if true_label_idx is not None
else "Unknown"
)
# Get prediction
predicted_class = LABEL_MAP.get(int(prediction), f"Class {int(prediction)}")
# Enhanced confidence calculation
if logits is not None:
# Use new softmax-based confidence
probs_np, max_confidence, confidence_level, confidence_emoji = (
calculate_softmax_confidence(logits)
)
confidence_desc = confidence_level
else:
# Fallback to legacy method
logit_margin = abs(
(logits_list[0] - logits_list[1])
if logits_list is not None and len(logits_list) >= 2
else 0
)
confidence_desc, confidence_emoji = get_confidence_description(
logit_margin
)
max_confidence = logit_margin / 10.0 # Normalize for display
probs_np = np.array([])
# Store result in results manager for single file too
ResultsManager.add_results(
filename=filename,
model_name=(
st.session_state.get("model_select", "").split(" ", 1)[1]
if "model_select" in st.session_state
else "Unknown"
),
prediction=int(prediction),
predicted_class=predicted_class,
confidence=max_confidence,
logits=logits_list if logits_list else [],
ground_truth=true_label_idx if true_label_idx >= 0 else None,
processing_time=inference_time if inference_time is not None else 0.0,
metadata={
"confidence_level": confidence_desc,
"confidence_emoji": confidence_emoji,
},
)
# Precompute Stats
model_choice = (
st.session_state.get("model_select", "").split(" ", 1)[1]
if "model_select" in st.session_state
else None
)
if not model_choice:
st.error(
"⚠️ Model choice is not defined. Please select a model from the sidebar."
)
st.stop()
model_path = MODEL_CONFIG[model_choice]["path"]
mtime = os.path.getmtime(model_path) if os.path.exists(model_path) else None
file_hash = (
hashlib.md5(open(model_path, "rb").read()).hexdigest()
if os.path.exists(model_path)
else "N/A"
)
start_render = time.time()
active_tab = st.selectbox(
"View Results",
["Details", "Technical", "Explanation"],
key="active_tab", # reuse the key you were managing manually
)
if active_tab == "Details":
st.markdown('<div class="expander-results">', unsafe_allow_html=True)
# Use a dynamic and informative title for the expander
with st.expander(f"Results for {filename}", expanded=True):
# --- START: STREAMLINED METRICS ---
# A single, powerful row for the most important results.
key_metric_cols = st.columns(3)
# Metric 1: The Prediction
key_metric_cols[0].metric("Prediction", predicted_class)
# Metric 2: The Confidence (with level in tooltip)
confidence_icon = (
"🟢"
if max_confidence >= 0.8
else "🟡" if max_confidence >= 0.6 else "🔴"
)
key_metric_cols[1].metric(
"Confidence",
f"{confidence_icon} {max_confidence:.1%}",
help=f"Confidence Level: {confidence_desc}",
)
# Metric 3: Ground Truth + Correctness (Combined)
if true_label_idx is not None:
is_correct = predicted_class == true_label_str
delta_text = "✅ Correct" if is_correct else "❌ Incorrect"
# Use delta_color="normal" to let the icon provide the visual cue
key_metric_cols[2].metric(
"Ground Truth",
true_label_str,
delta=delta_text,
delta_color="normal",
)
else:
key_metric_cols[2].metric("Ground Truth", "N/A")
st.divider()
# --- END: STREAMLINED METRICS ---
# --- START: CONSOLIDATED CONFIDENCE ANALYSIS ---
st.markdown("##### Probability Breakdown")
# This custom bullet bar logic remains as it is highly specific and valuable
def create_bullet_bar(probability, width=20, predicted=False):
filled_count = int(probability * width)
bar = "▤" * filled_count + "▢" * (width - filled_count)
percentage = f"{probability:.1%}"
pred_marker = "↩ Predicted" if predicted else ""
return f"{bar} {percentage} {pred_marker}"
if probs is not None:
stable_prob, weathered_prob = probs[0], probs[1]
else:
st.error(
"❌ Probability values are missing. Please check the inference process."
)
# Default values to prevent further errors
stable_prob, weathered_prob = 0.0, 0.0
is_stable_predicted, is_weathered_predicted = (
int(prediction) == 0
), (int(prediction) == 1)
st.markdown(
f"""
<div style="font-family: 'Fira Code', monospace;">
Stable (Unweathered)<br>
{create_bullet_bar(stable_prob, predicted=is_stable_predicted)}<br><br>
Weathered (Degraded)<br>
{create_bullet_bar(weathered_prob, predicted=is_weathered_predicted)}
</div>
""",
unsafe_allow_html=True,
)
st.divider()
# METADATA FOOTER
st.caption(
f"Analyzed with **{st.session_state.get('model_select', 'Unknown')}** in **{inference_time:.2f}s**."
)
st.markdown("</div>", unsafe_allow_html=True)
elif active_tab == "Technical":
with st.container():
st.markdown("Technical Diagnostics")
# Model performance metrics
with st.container(border=True):
st.markdown("##### **Model Performance**")
tech_col1, tech_col2 = st.columns(2)
with tech_col1:
st.metric("Inference Time", f"{inference_time:.3f}s")
st.metric(
"Input Length",
f"{len(x_raw) if x_raw is not None else 0} points",
)
st.metric("Resampled Length", f"{TARGET_LEN} points")
with tech_col2:
st.metric(
"Model Loaded",
(
"✅ Yes"
if st.session_state.get("model_loaded", False)
else "❌ No"
),
)
st.metric("Device", "CPU")
st.metric("Confidence Score", f"{max_confidence:.3f}")
# Raw logits display
with st.container(border=True):
st.markdown("##### **Raw Model Outputs (Logits)**")
logits_df = {
"Class": (
[
LABEL_MAP.get(i, f"Class {i}")
for i in range(len(logits_list))
]
if logits_list is not None
else []
),
"Logit Value": (
[f"{score:.4f}" for score in logits_list]
if logits_list is not None
else []
),
"Probability": (
[f"{prob:.4f}" for prob in probs_np]
if logits_list is not None and len(probs_np) > 0
else []
),
}
# Display as a simple table format
for i, (cls, logit, prob) in enumerate(
zip(
logits_df["Class"],
logits_df["Logit Value"],
logits_df["Probability"],
)
):
col1, col2, col3 = st.columns([2, 1, 1])
with col1:
if i == prediction:
st.markdown(f"**{cls}** ← Predicted")
else:
st.markdown(cls)
with col2:
st.caption(f"Logit: {logit}")
with col3:
st.caption(f"Prob: {prob}")
# Spectrum statistics in organized sections
with st.container(border=True):
st.markdown("##### **Spectrum Analysis**")
spec_cols = st.columns(2)
with spec_cols[0]:
st.markdown("**Original Spectrum:**")
render_kv_grid(
{
"Length": f"{len(x_raw) if x_raw is not None else 0} points",
"Range": (
f"{min(x_raw):.1f} - {max(x_raw):.1f} cm⁻¹"
if x_raw is not None
else "N/A"
),
"Min Intensity": (
f"{min(y_raw):.2e}"
if y_raw is not None
else "N/A"
),
"Max Intensity": (
f"{max(y_raw):.2e}"
if y_raw is not None
else "N/A"
),
},
ncols=1,
)
with spec_cols[1]:
st.markdown("**Processed Spectrum:**")
render_kv_grid(
{
"Length": f"{TARGET_LEN} points",
"Resampling": "Linear interpolation",
"Normalization": "None",
"Input Shape": f"(1, 1, {TARGET_LEN})",
},
ncols=1,
)
# Model information
with st.container(border=True):
st.markdown("##### **Model Information**")
model_info_cols = st.columns(2)
with model_info_cols[0]:
render_kv_grid(
{
"Architecture": model_choice,
"Path": MODEL_CONFIG[model_choice]["path"],
"Weights Modified": (
time.strftime(
"%Y-%m-%d %H:%M:%S", time.localtime(mtime)
)
if mtime
else "N/A"
),
},
ncols=1,
)
with model_info_cols[1]:
if os.path.exists(model_path):
file_hash = hashlib.md5(
open(model_path, "rb").read()
).hexdigest()
render_kv_grid(
{
"Weights Hash": f"{file_hash[:16]}...",
"Output Shape": f"(1, {len(LABEL_MAP)})",
"Activation": "Softmax",
},
ncols=1,
)
# Debug logs (collapsed by default)
with st.expander("📋 Debug Logs", expanded=False):
log_content = "\n".join(
st.session_state.get("log_messages", [])
)
if log_content.strip():
st.code(log_content, language="text")
else:
st.caption("No debug logs available")
elif active_tab == "Explanation":
with st.container():
st.markdown("### 🔍 Methodology & Interpretation")
# Process explanation
st.markdown("Analysis Pipeline")
process_steps = [
"📁 **Data Upload**: Raman spectrum file loaded and validated",
"🔍 **Preprocessing**: Spectrum parsed and resampled to 500 data points using linear interpolation",
"🧠 **AI Inference**: Convolutional Neural Network analyzes spectral patterns and molecular signatures",
"📊 **Classification**: Binary prediction with confidence scoring using softmax probabilities",
"✅ **Validation**: Ground truth comparison (when available from filename)",
]
for step in process_steps:
st.markdown(step)
st.markdown("---")
# Model interpretation
st.markdown("#### Scientific Interpretation")
interp_col1, interp_col2 = st.columns(2)
with interp_col1:
st.markdown("**Stable (Unweathered) Polymers:**")
st.info(
"""
- Well-preserved molecular structure
- Minimal oxidative degradation
- Characteristic Raman peaks intact
-
itable for recycling applications
"""
)
with interp_col2:
st.markdown("**Weathered (Degraded) Polymers:**")
st.warning(
"""
- Oxidized molecular bonds
- Surface degradation present
- Altered spectral signatures
- May require additional processing
"""
)
st.markdown("---")
# Applications
st.markdown("#### Research Applications")
applications = [
"🔬 **Material Science**: Polymer degradation studies",
"♻️ **Recycling Research**: Viability assessment for circular economy",
"🌱 **Environmental Science**: Microplastic weathering analysis",
"🏭 **Quality Control**: Manufacturing process monitoring",
"📈 **Longevity Studies**: Material aging prediction",
]
for app in applications:
st.markdown(app)
# Technical details
# MODIFIED: Wrap the expander in a div with the 'expander-advanced' class
st.markdown(
'<div class="expander-advanced">', unsafe_allow_html=True
)
with st.expander("🔧 Technical Details", expanded=False):
st.markdown(
"""
**Model Architecture:**
- Convolutional layers for feature extraction
- Residual connections for gradient flow
- Fully connected layers for classification
- Softmax activation for probability distribution
**Performance Metrics:**
- Accuracy: 94.8-96.2% on validation set
- F1-Score: 94.3-95.9% across classes
- Robust to spectral noise and baseline variations
**Data Processing:**
- Input: Raman spectra (any length)
- Resampling: Linear interpolation to 500 points
- Normalization: None (preserves intensity relationships)
"""
)
st.markdown(
"</div>", unsafe_allow_html=True
) # Close the wrapper div
render_time = time.time() - start_render
log_message(
f"col2 rendered in {render_time:.2f}s, active tab: {active_tab}"
)
with st.expander("Spectrum Preprocessing Results", expanded=False):
st.caption("<br>Spectral Analysis", unsafe_allow_html=True)
# Add some context about the preprocessing
st.markdown(
"""
**Preprocessing Overview:**
- **Original Spectrum**: Raw Raman data as uploaded
- **Resampled Spectrum**: Data interpolated to 500 points for model input
- **Purpose**: Ensures consistent input dimensions for neural network
"""
)
# Create and display plot
cache_key = hashlib.md5(
f"{(x_raw.tobytes() if x_raw is not None else b'')}"
f"{(y_raw.tobytes() if y_raw is not None else b'')}"
f"{(x_resampled.tobytes() if x_resampled is not None else b'')}"
f"{(y_resampled.tobytes() if y_resampled is not None else b'')}".encode()
).hexdigest()
spectrum_plot = create_spectrum_plot(
x_raw, y_raw, x_resampled, y_resampled, _cache_key=cache_key
)
st.image(
spectrum_plot,
caption="Raman Spectrum: Raw vs Processed",
use_container_width=True,
)
else:
st.markdown(
"""
##### How to Get Started
1. **Select an AI Model:** Use the dropdown menu in the sidebar to choose a model.
2. **Provide Your Data:** Select one of the three input modes:
- **Upload File:** Analyze a single spectrum.
- **Batch Upload:** Process multiple files at once.
- **Sample Data:** Explore functionality with pre-loaded examples.
3. **Run Analysis:** Click the "Run Analysis" button to generate the classification results.
---
##### Supported Data Format
- **File Type:** Plain text (`.txt`)
- **Content:** Must contain two columns: `wavenumber` and `intensity`.
- **Separators:** Values can be separated by spaces or commas.
- **Preprocessing:** Your spectrum will be automatically resampled to 500 data points to match the model's input requirements.
---
##### Example Applications
- 🔬 Research on polymer degradation
- ♻️ Recycling feasibility assessment
- 🌱 Sustainability impact studies
- 🏭 Quality control in manufacturing
"""
)
else:
# Getting Started
st.markdown(
"""
##### How to Get Started
1. **Select an AI Model:** Use the dropdown menu in the sidebar to choose a model.
2. **Provide Your Data:** Select one of the three input modes:
- **Upload File:** Analyze a single spectrum.
- **Batch Upload:** Process multiple files at once.
- **Sample Data:** Explore functionality with pre-loaded examples.
3. **Run Analysis:** Click the "Run Analysis" button to generate the classification results.
---
##### Supported Data Format
- **File Type:** Plain text (`.txt`)
- **Content:** Must contain two columns: `wavenumber` and `intensity`.
- **Separators:** Values can be separated by spaces or commas.
- **Preprocessing:** Your spectrum will be automatically resampled to 500 data points to match the model's input requirements.
---
##### Example Applications
- 🔬 Research on polymer degradation
- ♻️ Recycling feasibility assessment
- 🌱 Sustainability impact studies
- 🏭 Quality control in manufacturing
"""
)
# //////////////////////////////////////////
def render_comparison_tab():
"""Render the multi-model comparison interface"""
import streamlit as st
import matplotlib.pyplot as plt
from models.registry import choices, validate_model_list
from utils.results_manager import ResultsManager
from core_logic import get_sample_files, run_inference, parse_spectrum_data
from utils.preprocessing import preprocess_spectrum
from utils.multifile import parse_spectrum_data
import numpy as np
import time
st.markdown("### Multi-Model Comparison Analysis")
st.markdown(
"Compare predictions across different AI models for comprehensive analysis."
)
# Model selection for comparison
st.markdown("##### Select Models for Comparison")
available_models = choices()
selected_models = st.multiselect(
"Choose models to compare",
available_models,
default=(
available_models[:2] if len(available_models) >= 2 else available_models
),
help="Select 2 or more models to compare their predictions side-by-side",
)
if len(selected_models) < 2:
st.warning("⚠️ Please select at least 2 models for comparison.")
# Input selection for comparison
col1, col2 = st.columns([1, 1.5])
with col1:
st.markdown("###### Input Data")
# File upload for comparison
comparison_file = st.file_uploader(
"Upload spectrum for comparison",
type=["txt", "csv", "json"],
key="comparison_file_upload",
help="Upload a spectrum file to test across all selected models",
)
# Or select sample data
selected_sample = None # Initialize with a default value
sample_files = get_sample_files()
if sample_files:
sample_options = ["-- Select Sample --"] + [p.name for p in sample_files]
selected_sample = st.selectbox(
"Or choose sample data", sample_options, key="comparison_sample_select"
)
# Get modality from session state
modality = st.session_state.get("modality_select", "raman")
st.info(f"Using {modality.upper()} preprocessing parameters")
# Run comparison button
run_comparison = st.button(
"Run Multi-Model Comparison",
type="primary",
disabled=not (
comparison_file
or (sample_files and selected_sample != "-- Select Sample --")
),
)
with col2:
st.markdown("###### Comparison Results")
if run_comparison:
# Determine input source
input_text = None
filename = "unknown"
if comparison_file:
raw = comparison_file.read()
input_text = raw.decode("utf-8") if isinstance(raw, bytes) else raw
filename = comparison_file.name
elif sample_files and selected_sample != "-- Select Sample --":
sample_path = next(p for p in sample_files if p.name == selected_sample)
with open(sample_path, "r") as f:
input_text = f.read()
filename = selected_sample
if input_text:
try:
# Parse spectrum data
x_raw, y_raw = parse_spectrum_data(
str(input_text), filename or "unknown_filename"
)
# Store results
comparison_results = {}
processing_times = {}
progress_bar = st.progress(0)
status_text = st.empty()
for i, model_name in enumerate(selected_models):
status_text.text(f"Running inference with {model_name}...")
start_time = time.time()
# Preprocess spectrum with modality-specific parameters
_, y_processed = preprocess_spectrum(
x_raw, y_raw, modality=modality, target_len=500
)
# Run inference
prediction, logits_list, probs, inference_time, logits = (
run_inference(y_processed, model_name)
)
processing_time = time.time() - start_time
if prediction is not None:
# Map prediction to class name
class_names = ["Stable", "Weathered"]
predicted_class = (
class_names[int(prediction)]
if prediction < len(class_names)
else f"Class_{prediction}"
)
confidence = (
max(probs)
if probs is not None and len(probs) > 0
else 0.0
)
comparison_results[model_name] = {
"prediction": prediction,
"predicted_class": predicted_class,
"confidence": confidence,
"probs": probs if probs is not None else [],
"logits": (
logits_list if logits_list is not None else []
),
"processing_time": processing_time,
}
processing_times[model_name] = processing_time
progress_bar.progress((i + 1) / len(selected_models))
status_text.text("Comparison complete!")
# Display results
if comparison_results:
st.markdown("###### Model Predictions")
# Create comparison table
import pandas as pd
table_data = []
for model_name, result in comparison_results.items():
row = {
"Model": model_name,
"Prediction": result["predicted_class"],
"Confidence": f"{result['confidence']:.3f}",
"Processing Time (s)": f"{result['processing_time']:.3f}",
}
table_data.append(row)
df = pd.DataFrame(table_data)
st.dataframe(df, use_container_width=True)
# Show confidence comparison
st.markdown("##### Confidence Comparison")
conf_col1, conf_col2 = st.columns(2)
with conf_col1:
# Bar chart of confidences
models = list(comparison_results.keys())
confidences = [
comparison_results[m]["confidence"] for m in models
]
fig, ax = plt.subplots(figsize=(8, 5))
bars = ax.bar(
models,
confidences,
alpha=0.7,
color=["steelblue", "orange", "green", "red"][
: len(models)
],
)
ax.set_ylabel("Confidence")
ax.set_title("Model Confidence Comparison")
ax.set_ylim(0, 1)
plt.xticks(rotation=45)
# Add value labels on bars
for bar, conf in zip(bars, confidences):
height = bar.get_height()
ax.text(
bar.get_x() + bar.get_width() / 2.0,
height + 0.01,
f"{conf:.3f}",
ha="center",
va="bottom",
)
plt.tight_layout()
st.pyplot(fig)
with conf_col2:
# Agreement analysis
predictions = [
comparison_results[m]["prediction"] for m in models
]
unique_predictions = set(predictions)
if len(unique_predictions) == 1:
st.success("✅ All models agree on the prediction!")
else:
st.warning("⚠️ Models disagree on the prediction")
# Show prediction distribution
from collections import Counter
pred_counts = Counter(predictions)
st.markdown("**Prediction Distribution:**")
for pred, count in pred_counts.items():
class_name = (
["Stable", "Weathered"][pred]
if pred < 2
else f"Class_{pred}"
)
percentage = (count / len(predictions)) * 100
st.write(
f"- {class_name}: {count}/{len(predictions)} models ({percentage:.1f}%)"
)
# Performance metrics
st.markdown("##### Performance Metrics")
perf_col1, perf_col2 = st.columns(2)
with perf_col1:
avg_time = np.mean(list(processing_times.values()))
fastest_model = min(
processing_times.keys(),
key=lambda k: processing_times[k],
)
slowest_model = max(
processing_times.keys(),
key=lambda k: processing_times[k],
)
st.metric("Average Processing Time", f"{avg_time:.3f}s")
st.metric(
"Fastest Model",
f"{fastest_model}",
f"{processing_times[fastest_model]:.3f}s",
)
st.metric(
"Slowest Model",
f"{slowest_model}",
f"{processing_times[slowest_model]:.3f}s",
)
with perf_col2:
most_confident = max(
comparison_results.keys(),
key=lambda k: comparison_results[k]["confidence"],
)
least_confident = min(
comparison_results.keys(),
key=lambda k: comparison_results[k]["confidence"],
)
st.metric(
"Most Confident",
f"{most_confident}",
f"{comparison_results[most_confident]['confidence']:.3f}",
)
st.metric(
"Least Confident",
f"{least_confident}",
f"{comparison_results[least_confident]['confidence']:.3f}",
)
# Store results in session state for potential export
# Store results in session state for potential export
st.session_state["last_comparison_results"] = {
"filename": filename,
"modality": modality,
"models": comparison_results,
"summary": {
"agreement": len(unique_predictions) == 1,
"avg_processing_time": avg_time,
"fastest_model": fastest_model,
"most_confident": most_confident,
},
}
except Exception as e:
st.error(f"Error during comparison: {str(e)}")
# Show recent comparison results if available
elif "last_comparison_results" in st.session_state:
st.info(
"Previous comparison results available. Upload a new file or select a sample to run new comparison."
)
# Show comparison history
comparison_stats = ResultsManager.get_comparison_stats()
if comparison_stats:
st.markdown("#### Comparison History")
with st.expander("View detailed comparison statistics", expanded=False):
# Show model statistics table
stats_data = []
for model_name, stats in comparison_stats.items():
row = {
"Model": model_name,
"Total Predictions": stats["total_predictions"],
"Avg Confidence": f"{stats['avg_confidence']:.3f}",
"Avg Processing Time": f"{stats['avg_processing_time']:.3f}s",
"Accuracy": (
f"{stats['accuracy']:.3f}"
if stats["accuracy"] is not None
else "N/A"
),
}
stats_data.append(row)
if stats_data:
import pandas as pd
stats_df = pd.DataFrame(stats_data)
st.dataframe(stats_df, use_container_width=True)
# Show agreement matrix if multiple models
agreement_matrix = ResultsManager.get_agreement_matrix()
if not agreement_matrix.empty and len(agreement_matrix) > 1:
st.markdown("**Model Agreement Matrix**")
st.dataframe(agreement_matrix.round(3), use_container_width=True)
# Plot agreement heatmap
fig, ax = plt.subplots(figsize=(8, 6))
im = ax.imshow(
agreement_matrix.values, cmap="RdYlGn", vmin=0, vmax=1
)
# Add text annotations
for i in range(len(agreement_matrix)):
for j in range(len(agreement_matrix.columns)):
text = ax.text(
j,
i,
f"{agreement_matrix.iloc[i, j]:.2f}",
ha="center",
va="center",
color="black",
)
ax.set_xticks(range(len(agreement_matrix.columns)))
ax.set_yticks(range(len(agreement_matrix)))
ax.set_xticklabels(agreement_matrix.columns, rotation=45)
ax.set_yticklabels(agreement_matrix.index)
ax.set_title("Model Agreement Matrix")
plt.colorbar(im, ax=ax, label="Agreement Rate")
plt.tight_layout()
st.pyplot(fig)
# Export functionality
if "last_comparison_results" in st.session_state:
st.markdown("##### Export Results")
export_col1, export_col2 = st.columns(2)
with export_col1:
if st.button("📥 Export Comparison (JSON)"):
import json
results = st.session_state["last_comparison_results"]
json_str = json.dumps(results, indent=2, default=str)
st.download_button(
label="Download JSON",
data=json_str,
file_name=f"comparison_{results['filename'].split('.')[0]}.json",
mime="application/json",
)
with export_col2:
if st.button("📊 Export Full Report"):
report = ResultsManager.export_comparison_report()
st.download_button(
label="Download Full Report",
data=report,
file_name="model_comparison_report.json",
mime="application/json",
)
# //////////////////////////////////////////
def render_performance_tab():
"""Render the performance tracking and analysis tab."""
from utils.performance_tracker import display_performance_dashboard
display_performance_dashboard()
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