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Fashion_CNN_Interpretability_Matrix / src /app.py

Update src/app.py

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import streamlit as st
import torch
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from pipeline import AdvancedFashionCNN, ActivationTelemetryEngine, transform_custom_image

# --- DASHBOARD LAYOUT MANAGEMENT ---
st.set_page_config(
    page_title="AI Portfolio | Computer Vision Explainer Engine",
    page_icon="🧠",
    layout="wide"
)

# Load global definitions
CLASS_NAMES = ["T-shirt/top", "Trouser", "Pullover", "Dress", "Coat", "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"]

@st.cache_resource
def bootstrap_model_engine():
    """Compiles framework layers and acts as a safe memory cache layer."""
    instance = AdvancedFashionCNN()
    # Weights initialize gracefully for telemetry presentations
    return instance, ActivationTelemetryEngine(instance)

model, tele_engine = bootstrap_model_engine()

# --- SIDEBAR INTERFACE STRUCTURE ---
with st.sidebar:
    st.title("PKRAMAN")
    st.caption("🚀 Deep Learning & Computer Vision Research Engineer")
    st.markdown("🌐 [GitHub](https://github.com) | 💼 [LinkedIn](https://linkedin.com)")
    st.markdown("---")
    
    st.subheader("🛠️ Core Architecture Features")
    st.markdown("""
    - **Squeeze-and-Excitation (SE):** Multi-channel attention mapping.
    - **Grad-CAM Tracking:** Latent-space backpropagation.
    - **Residual Paths:** Multi-layer identity mappings.
    """)
    st.markdown("---")
    
    input_method = st.radio("Asset Ingestion Strategy", ["Database Testing Samples", "Custom Image Upload"])

# --- MAIN SCREEN PORTFOLIO HERO SEGMENT ---
st.title("🧠 Advanced Explainer Engine: Interpretability Matrix for Fashion CNNs")
st.caption("Interactive Neural Layer Visualization & Explainer Engine for Production Models")

# Metric KPIs
m1, m2, m3 = st.columns(3)
m1.metric("Feature Extractor Layer", "Residual Conv + SE-Attention", "Dynamic Hooks Active")
m2.metric("Gradient Localization Engine", "Grad-CAM Backend", "Target: Layer 3 Conv")
m3.metric("Dataset Domain Space", "FashionMNIST", "10-Class Optimization Loop")

st.markdown("---")

# Setup Image Core Inputs
image_tensor, reference_label = None, None

if input_method == "Database Testing Samples":
    # Download baseline test sets for quick selection verification
    test_dataset = torchvision.datasets.FashionMNIST(
        root='./data', train=False, download=True,
        transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])
    )
    sample_index = st.slider("Select Sample Index from Database Validation Set", 0, len(test_dataset)-1, 16)
    image_tensor, reference_label = test_dataset[sample_index]
else:
    uploaded_file = st.file_uploader("Upload an Image File (Auto-Grayscale & Resized to 28x28)", type=["jpg", "jpeg", "png"])
    if uploaded_file is not None:
        try:
            image_tensor = transform_custom_image(uploaded_file)
        except Exception as e:
            st.error(f"Ingestion compilation format error: {e}")

# --- METRIC GENERATION DISPLAY SEQUENCE ---
if image_tensor is not None:
    # Forward pass predictions
    with torch.no_grad():
        logits = model(image_tensor.unsqueeze(0).to(tele_engine.device))
        probs = torch.softmax(logits, dim=1).squeeze().cpu().numpy()
    
    predicted_class = np.argmax(probs)
    confidence = probs[predicted_class]

    # Run interpretability computations
    saliency_map = tele_engine.compute_saliency(image_tensor)
    gradcam_map = tele_engine.compute_gradcam(image_tensor)

    # UI presentation space
    col_img, col_metrics = st.columns([1.2, 0.8])
    
    with col_img:
        st.subheader("🎯 Visual Explainer Core Diagnostics")
        fig, axes = plt.subplots(1, 3, figsize=(15, 5))
        
        # Raw Display Channel
        raw_img = image_tensor.squeeze().numpy()
        axes[0].imshow(raw_img, cmap='gray')
        axes[0].set_title("Input Raw Channel")
        axes[0].axis('off')

        # Saliency Visual Matrix
        axes[1].imshow(saliency_map, cmap='hot')
        axes[1].set_title("Pixel-Level Saliency")
        axes[1].axis('off')

        # Grad-CAM Overlay Integration Map
        axes[2].imshow(raw_img, cmap='gray')
        axes[2].imshow(gradcam_map, cmap='jet', alpha=0.5)
        axes[2].set_title("Grad-CAM Latent Attentions")
        axes[2].axis('off')

        st.pyplot(fig)

    with col_metrics:
        st.subheader("📋 Inference Analysis Profiles")
        
        st.metric("Predicted Target Class Name", f"{CLASS_NAMES[predicted_class]}")
        st.metric("Model Execution Certainty Score", f"{confidence:.2%}")
        
        if reference_label is not None:
            st.metric("Ground-Truth Database Metric", f"{CLASS_NAMES[reference_label]}")
            if predicted_class == reference_label:
                st.success("🟢 True Ingestion Classification Verified.")
            else:
                st.error("🔴 Model Space Misclassification Variance Documented.")
                
        # Mini probability trace tracking distribution
        st.markdown("**Output Tensor Class Distributions:**")
        chart_data = pd.DataFrame({"Activation Match": probs}, index=CLASS_NAMES)
        st.bar_chart(chart_data)
else:
    st.info("⚡ Awaiting input array matrices. Choose an engineering input strategy or index sample from the sidebar.")

	import streamlit as st
	import torch
	import torchvision
	import torchvision.transforms as transforms
	import matplotlib.pyplot as plt
	import numpy as np
	import pandas as pd
	from pipeline import AdvancedFashionCNN, ActivationTelemetryEngine, transform_custom_image

	# --- DASHBOARD LAYOUT MANAGEMENT ---
	st.set_page_config(
	page_title="AI Portfolio \| Computer Vision Explainer Engine",
	page_icon="🧠",
	layout="wide"
	)

	# Load global definitions
	CLASS_NAMES = ["T-shirt/top", "Trouser", "Pullover", "Dress", "Coat", "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"]

	@st.cache_resource
	def bootstrap_model_engine():
	"""Compiles framework layers and acts as a safe memory cache layer."""
	instance = AdvancedFashionCNN()
	# Weights initialize gracefully for telemetry presentations
	return instance, ActivationTelemetryEngine(instance)

	model, tele_engine = bootstrap_model_engine()

	# --- SIDEBAR INTERFACE STRUCTURE ---
	with st.sidebar:
	st.title("PKRAMAN")
	st.caption("🚀 Deep Learning & Computer Vision Research Engineer")
	st.markdown("🌐 [GitHub](https://github.com) \| 💼 [LinkedIn](https://linkedin.com)")
	st.markdown("---")

	st.subheader("🛠️ Core Architecture Features")
	st.markdown("""
	- Squeeze-and-Excitation (SE): Multi-channel attention mapping.
	- Grad-CAM Tracking: Latent-space backpropagation.
	- Residual Paths: Multi-layer identity mappings.
	""")
	st.markdown("---")

	input_method = st.radio("Asset Ingestion Strategy", ["Database Testing Samples", "Custom Image Upload"])

	# --- MAIN SCREEN PORTFOLIO HERO SEGMENT ---
	st.title("🧠 Advanced Explainer Engine: Interpretability Matrix for Fashion CNNs")
	st.caption("Interactive Neural Layer Visualization & Explainer Engine for Production Models")

	# Metric KPIs
	m1, m2, m3 = st.columns(3)
	m1.metric("Feature Extractor Layer", "Residual Conv + SE-Attention", "Dynamic Hooks Active")
	m2.metric("Gradient Localization Engine", "Grad-CAM Backend", "Target: Layer 3 Conv")
	m3.metric("Dataset Domain Space", "FashionMNIST", "10-Class Optimization Loop")

	st.markdown("---")

	# Setup Image Core Inputs
	image_tensor, reference_label = None, None

	if input_method == "Database Testing Samples":
	# Download baseline test sets for quick selection verification
	test_dataset = torchvision.datasets.FashionMNIST(
	root='./data', train=False, download=True,
	transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])
	)
	sample_index = st.slider("Select Sample Index from Database Validation Set", 0, len(test_dataset)-1, 16)
	image_tensor, reference_label = test_dataset[sample_index]
	else:
	uploaded_file = st.file_uploader("Upload an Image File (Auto-Grayscale & Resized to 28x28)", type=["jpg", "jpeg", "png"])
	if uploaded_file is not None:
	try:
	image_tensor = transform_custom_image(uploaded_file)
	except Exception as e:
	st.error(f"Ingestion compilation format error: {e}")

	# --- METRIC GENERATION DISPLAY SEQUENCE ---
	if image_tensor is not None:
	# Forward pass predictions
	with torch.no_grad():
	logits = model(image_tensor.unsqueeze(0).to(tele_engine.device))
	probs = torch.softmax(logits, dim=1).squeeze().cpu().numpy()

	predicted_class = np.argmax(probs)
	confidence = probs[predicted_class]

	# Run interpretability computations
	saliency_map = tele_engine.compute_saliency(image_tensor)
	gradcam_map = tele_engine.compute_gradcam(image_tensor)

	# UI presentation space
	col_img, col_metrics = st.columns([1.2, 0.8])

	with col_img:
	st.subheader("🎯 Visual Explainer Core Diagnostics")
	fig, axes = plt.subplots(1, 3, figsize=(15, 5))

	# Raw Display Channel
	raw_img = image_tensor.squeeze().numpy()
	axes[0].imshow(raw_img, cmap='gray')
	axes[0].set_title("Input Raw Channel")
	axes[0].axis('off')

	# Saliency Visual Matrix
	axes[1].imshow(saliency_map, cmap='hot')
	axes[1].set_title("Pixel-Level Saliency")
	axes[1].axis('off')

	# Grad-CAM Overlay Integration Map
	axes[2].imshow(raw_img, cmap='gray')
	axes[2].imshow(gradcam_map, cmap='jet', alpha=0.5)
	axes[2].set_title("Grad-CAM Latent Attentions")
	axes[2].axis('off')

	st.pyplot(fig)

	with col_metrics:
	st.subheader("📋 Inference Analysis Profiles")

	st.metric("Predicted Target Class Name", f"{CLASS_NAMES[predicted_class]}")
	st.metric("Model Execution Certainty Score", f"{confidence:.2%}")

	if reference_label is not None:
	st.metric("Ground-Truth Database Metric", f"{CLASS_NAMES[reference_label]}")
	if predicted_class == reference_label:
	st.success("🟢 True Ingestion Classification Verified.")
	else:
	st.error("🔴 Model Space Misclassification Variance Documented.")

	# Mini probability trace tracking distribution
	st.markdown("Output Tensor Class Distributions:")
	chart_data = pd.DataFrame({"Activation Match": probs}, index=CLASS_NAMES)
	st.bar_chart(chart_data)
	else:
	st.info("⚡ Awaiting input array matrices. Choose an engineering input strategy or index sample from the sidebar.")