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import streamlit as st |
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from pathlib import Path |
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import matplotlib.pyplot as plt |
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import numpy as np |
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from sklearn.metrics import confusion_matrix |
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from app.inference import load_models, predict_image, evaluate_dataset |
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from app.config import DATA_ROOT, IMAGE_EXTENSIONS |
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PROJECT_ROOT = Path(__file__).resolve().parent |
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TRAINING_REPORT_TEXT = """ precision recall f1-score support |
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pins_Adriana Lima 1.00 1.00 1.00 32 |
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pins_Alex Lawther 1.00 1.00 1.00 23 |
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pins_Alexandra Daddario 0.97 1.00 0.99 34 |
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pins_Alvaro Morte 1.00 1.00 1.00 30 |
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pins_Amanda Crew 1.00 1.00 1.00 30 |
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pins_Andy Samberg 1.00 1.00 1.00 29 |
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pins_Anne Hathaway 0.97 1.00 0.98 30 |
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pins_Anthony Mackie 1.00 1.00 1.00 30 |
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pins_Avril Lavigne 1.00 0.96 0.98 24 |
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pins_Ben Affleck 1.00 1.00 1.00 30 |
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pins_Bill Gates 1.00 1.00 1.00 30 |
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pins_Bobby Morley 0.97 1.00 0.98 30 |
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pins_Brenton Thwaites 0.97 0.97 0.97 31 |
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pins_Brian J. Smith 1.00 1.00 1.00 30 |
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pins_Brie Larson 1.00 0.92 0.96 25 |
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pins_Chris Evans 1.00 1.00 1.00 25 |
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pins_Chris Hemsworth 1.00 1.00 1.00 24 |
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pins_Chris Pratt 1.00 1.00 1.00 26 |
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pins_Christian Bale 1.00 1.00 1.00 23 |
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pins_Cristiano Ronaldo 1.00 1.00 1.00 30 |
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pins_Danielle Panabaker 0.96 1.00 0.98 27 |
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pins_Dominic Purcell 1.00 1.00 1.00 30 |
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pins_Dwayne Johnson 1.00 1.00 1.00 30 |
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pins_Eliza Taylor 1.00 1.00 1.00 24 |
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pins_Elizabeth Lail 1.00 1.00 1.00 23 |
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pins_Emilia Clarke 1.00 0.97 0.98 31 |
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pins_Emma Stone 1.00 0.97 0.98 30 |
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pins_Emma Watson 0.94 1.00 0.97 32 |
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pins_Gwyneth Paltrow 1.00 1.00 1.00 28 |
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pins_Henry Cavil 1.00 1.00 1.00 29 |
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pins_Hugh Jackman 1.00 0.96 0.98 27 |
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pins_Inbar Lavi 1.00 1.00 1.00 30 |
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pins_Irina Shayk 0.96 1.00 0.98 23 |
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pins_Jake Mcdorman 1.00 1.00 1.00 24 |
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pins_Jason Momoa 1.00 1.00 1.00 28 |
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pins_Jennifer Lawrence 0.96 1.00 0.98 27 |
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pins_Jeremy Renner 1.00 1.00 1.00 25 |
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pins_Jessica Barden 1.00 0.93 0.97 30 |
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pins_Jimmy Fallon 1.00 1.00 1.00 30 |
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pins_Johnny Depp 1.00 1.00 1.00 27 |
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pins_Josh Radnor 1.00 1.00 1.00 30 |
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pins_Katharine Mcphee 1.00 1.00 1.00 27 |
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pins_Katherine Langford 1.00 1.00 1.00 34 |
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pins_Keanu Reeves 1.00 1.00 1.00 24 |
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pins_Krysten Ritter 1.00 1.00 1.00 26 |
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pins_Leonardo DiCaprio 0.94 0.97 0.96 35 |
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pins_Lili Reinhart 0.96 1.00 0.98 22 |
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pins_Lindsey Morgan 0.93 1.00 0.96 25 |
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pins_Lionel Messi 1.00 1.00 1.00 30 |
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pins_Logan Lerman 1.00 0.97 0.98 32 |
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pins_Madelaine Petsch 0.97 1.00 0.98 29 |
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pins_Maisie Williams 1.00 1.00 1.00 29 |
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pins_Maria Pedraza 1.00 1.00 1.00 30 |
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pins_Marie Avgeropoulos 1.00 1.00 1.00 24 |
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pins_Mark Ruffalo 1.00 1.00 1.00 27 |
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pins_Mark Zuckerberg 1.00 1.00 1.00 30 |
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pins_Megan Fox 1.00 1.00 1.00 31 |
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pins_Miley Cyrus 1.00 1.00 1.00 27 |
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pins_Millie Bobby Brown 0.97 1.00 0.98 29 |
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pins_Morena Baccarin 1.00 1.00 1.00 26 |
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pins_Morgan Freeman 1.00 1.00 1.00 30 |
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pins_Nadia Hilker 1.00 1.00 1.00 30 |
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pins_Natalie Dormer 1.00 1.00 1.00 29 |
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pins_Natalie Portman 1.00 1.00 1.00 25 |
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pins_Neil Patrick Harris 1.00 1.00 1.00 30 |
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pins_Pedro Alonso 1.00 1.00 1.00 30 |
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pins_Penn Badgley 1.00 1.00 1.00 26 |
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pins_Rami Malek 1.00 1.00 1.00 24 |
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pins_Rebecca Ferguson 1.00 1.00 1.00 27 |
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pins_Richard Harmon 1.00 0.97 0.98 30 |
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pins_Rihanna 1.00 1.00 1.00 30 |
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pins_Robert De Niro 1.00 1.00 1.00 23 |
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pins_Robert Downey Jr 1.00 1.00 1.00 35 |
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pins_Sarah Wayne Callies 1.00 0.96 0.98 24 |
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pins_Selena Gomez 1.00 0.96 0.98 28 |
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pins_Shakira Isabel Mebarak 1.00 1.00 1.00 23 |
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pins_Sophie Turner 1.00 1.00 1.00 30 |
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pins_Stephen Amell 1.00 1.00 1.00 24 |
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pins_Taylor Swift 1.00 1.00 1.00 30 |
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pins_Tom Cruise 1.00 1.00 1.00 29 |
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pins_Tom Hardy 1.00 1.00 1.00 30 |
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pins_Tom Hiddleston 1.00 1.00 1.00 27 |
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pins_Tom Holland 1.00 0.96 0.98 28 |
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pins_Tuppence Middleton 1.00 1.00 1.00 30 |
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pins_Ursula Corbero 1.00 1.00 1.00 25 |
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pins_Wentworth Miller 1.00 1.00 1.00 27 |
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pins_Zac Efron 1.00 1.00 1.00 29 |
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pins_Zendaya 1.00 0.97 0.98 30 |
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pins_Zoe Saldana 1.00 0.96 0.98 28 |
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pins_alycia dabnem carey 1.00 1.00 1.00 32 |
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pins_amber heard 1.00 1.00 1.00 33 |
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pins_barack obama 1.00 1.00 1.00 30 |
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pins_barbara palvin 1.00 1.00 1.00 29 |
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pins_camila mendes 1.00 1.00 1.00 24 |
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pins_elizabeth olsen 1.00 0.94 0.97 33 |
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pins_ellen page 0.93 1.00 0.97 28 |
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pins_elon musk 1.00 1.00 1.00 30 |
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pins_gal gadot 0.97 0.97 0.97 30 |
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pins_grant gustin 1.00 1.00 1.00 27 |
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pins_jeff bezos 1.00 1.00 1.00 30 |
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pins_kiernen shipka 0.97 1.00 0.98 30 |
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pins_margot robbie 0.97 0.97 0.97 33 |
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pins_melissa fumero 1.00 1.00 1.00 23 |
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pins_scarlett johansson 1.00 0.97 0.98 30 |
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pins_tom ellis 0.97 1.00 0.99 34 |
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accuracy 0.99 2975 |
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macro avg 0.99 0.99 0.99 2975 |
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weighted avg 0.99 0.99 0.99 2975 |
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<Figure size 800x800 with 2 Axes> |
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5-fold CV: mean=0.9917 std=0.0013 |
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Saved centroids to embeddings_cache\\centroids.npy and classes to embeddings_cache\\classes.npy |
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Centroid baseline accuracy: 0.9870771569745344 |
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Suggested cosine threshold for open-set (approx TPR=0.95): 0.4617 |
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""" |
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PREDICTION_REPORT_TEXT = """Loading trained model... |
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✅ Model loaded. Can recognize 105 classes |
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Found 17534 images in human_face_dataset/pins_face_recognition |
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Processing images... |
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Predicting: 100%|██████████████████████████████████████████████████████████████| 17534/17534 [1:42:42<00:00, 2.85it/s] |
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================================================================================ |
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📊 PREDICTION SUMMARY |
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================================================================================ |
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✅ Total images processed: 17486 |
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✅ Correct predictions: 17442 (99.75%) |
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❌ Wrong predictions: 44 (0.25%) |
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📊 Overall Accuracy: 0.9975 (99.75%) |
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📊 Average confidence: 0.8239 |
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❌ Failed to process: 48 images |
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================================================================================ |
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❌ WRONG PREDICTIONS DETAILS (44 total) |
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================================================================================ |
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Classes with wrong predictions: |
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actual_class wrong_count predicted_as |
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pins_Brie Larson 4 pins_Emma Stone, pins_ellen page, pins_Jennifer Lawrence |
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pins_Jessica Barden 3 pins_Alex Lawther, pins_kiernen shipka, pins_Danielle Panabaker |
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pins_Logan Lerman 3 pins_Sarah Wayne Callies, pins_Leonardo DiCaprio, pins_Eliza Taylor |
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pins_scarlett johansson 2 pins_gal gadot, pins_Taylor Swift |
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pins_Emilia Clarke 2 pins_Marie Avgeropoulos, pins_Irina Shayk |
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pins_Brenton Thwaites 2 pins_Leonardo DiCaprio, pins_Bobby Morley |
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pins_elizabeth olsen 2 pins_ellen page, pins_Millie Bobby Brown |
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pins_Zendaya 2 pins_Lili Reinhart, pins_Selena Gomez |
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pins_Tom Holland 2 pins_Anne Hathaway, pins_Robert Downey Jr |
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pins_Tom Hiddleston 1 pins_Chris Evans |
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pins_Selena Gomez 1 pins_Emma Watson |
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pins_Taylor Swift 1 pins_Emma Stone |
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pins_Zoe Saldana 1 pins_Lindsey Morgan |
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pins_Richard Harmon 1 pins_Leonardo DiCaprio |
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pins_amber heard 1 pins_Brie Larson |
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pins_gal gadot 1 pins_Madelaine Petsch |
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pins_margot robbie 1 pins_Emma Watson |
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pins_Sarah Wayne Callies 1 pins_Lindsey Morgan |
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pins_Avril Lavigne 1 pins_Alexandra Daddario |
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pins_Natalie Portman 1 pins_Millie Bobby Brown |
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pins_Nadia Hilker 1 pins_Marie Avgeropoulos |
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pins_Marie Avgeropoulos 1 pins_Johnny Depp |
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pins_Lindsey Morgan 1 pins_Anne Hathaway |
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pins_Leonardo DiCaprio 1 pins_Brenton Thwaites |
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pins_Katherine Langford 1 pins_Madelaine Petsch |
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pins_Johnny Depp 1 pins_Leonardo DiCaprio |
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pins_Irina Shayk 1 pins_Maria Pedraza |
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pins_Hugh Jackman 1 pins_tom ellis |
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pins_Emma Stone 1 pins_margot robbie |
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pins_Chris Hemsworth 1 pins_Chris Evans |
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pins_Morena Baccarin 1 pins_camila mendes |
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-------------------------------------------------------------------------------- |
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Individual wrong predictions (showing first 20): |
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-------------------------------------------------------------------------------- |
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• amber heard214_323.jpg |
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Actual: pins_amber heard → Predicted: pins_Brie Larson (confidence: 0.275) |
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• Avril Lavigne238_664.jpg |
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Actual: pins_Avril Lavigne → Predicted: pins_Alexandra Daddario (confidence: 0.131) |
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• Brenton Thwaites46_885.jpg |
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Actual: pins_Brenton Thwaites → Predicted: pins_Leonardo DiCaprio (confidence: 0.273) |
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• Brenton Thwaites99_936.jpg |
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Actual: pins_Brenton Thwaites → Predicted: pins_Bobby Morley (confidence: 0.204) |
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• Brie Larson157_994.jpg |
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Actual: pins_Brie Larson → Predicted: pins_Emma Stone (confidence: 0.136) |
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• Brie Larson172_1007.jpg |
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Actual: pins_Brie Larson → Predicted: pins_ellen page (confidence: 0.253) |
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• Brie Larson187_1021.jpg |
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Actual: pins_Brie Larson → Predicted: pins_Jennifer Lawrence (confidence: 0.127) |
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• Brie Larson77_1088.jpg |
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Actual: pins_Brie Larson → Predicted: pins_margot robbie (confidence: 0.330) |
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• Chris Hemsworth1_384.jpg |
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Actual: pins_Chris Hemsworth → Predicted: pins_Chris Evans (confidence: 0.095) |
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• elizabeth olsen164_1173.jpg |
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Actual: pins_elizabeth olsen → Predicted: pins_ellen page (confidence: 0.355) |
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• elizabeth olsen170_1179.jpg |
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Actual: pins_elizabeth olsen → Predicted: pins_Millie Bobby Brown (confidence: 0.272) |
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• Emilia Clarke194_952.jpg |
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Actual: pins_Emilia Clarke → Predicted: pins_Marie Avgeropoulos (confidence: 0.150) |
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• Emilia Clarke48_1021.jpg |
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Actual: pins_Emilia Clarke → Predicted: pins_Irina Shayk (confidence: 0.069) |
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• Emma Stone36_1779.jpg |
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Actual: pins_Emma Stone → Predicted: pins_margot robbie (confidence: 0.282) |
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• gal gadot134_1690.jpg |
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Actual: pins_gal gadot → Predicted: pins_Madelaine Petsch (confidence: 0.234) |
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• Hugh Jackman118_1288.jpg |
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Actual: pins_Hugh Jackman → Predicted: pins_tom ellis (confidence: 0.128) |
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• Irina Shayk236_2335.jpg |
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Actual: pins_Irina Shayk → Predicted: pins_Maria Pedraza (confidence: 0.082) |
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• Jessica Barden211_1449.jpg |
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Actual: pins_Jessica Barden → Predicted: pins_Alex Lawther (confidence: 0.779) |
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• Jessica Barden31_1475.jpg |
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Actual: pins_Jessica Barden → Predicted: pins_kiernen shipka (confidence: 0.098) |
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• Jessica Barden34_1478.jpg |
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Actual: pins_Jessica Barden → Predicted: pins_Danielle Panabaker (confidence: 0.048) |
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... and 24 more wrong predictions (see CSV for details) |
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================================================================================ |
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🎯 CLASSES WITH 100% ACCURACY (74 classes) |
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================================================================================ |
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class_name total_count |
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pins_Adriana Lima 213 |
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pins_Millie Bobby Brown 191 |
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pins_Rihanna 132 |
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pins_Rebecca Ferguson 178 |
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pins_Rami Malek 160 |
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pins_Penn Badgley 171 |
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pins_Pedro Alonso 125 |
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pins_Neil Patrick Harris 116 |
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pins_Natalie Dormer 196 |
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pins_Morgan Freeman 102 |
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pins_Miley Cyrus 178 |
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pins_Keanu Reeves 158 |
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pins_Megan Fox 208 |
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pins_Mark Zuckerberg 95 |
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pins_Mark Ruffalo 177 |
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pins_Alex Lawther 152 |
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pins_Maisie Williams 193 |
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pins_Madelaine Petsch 192 |
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pins_Lionel Messi 86 |
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pins_Lili Reinhart 150 |
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... and 54 more classes |
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================================================================================ |
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⚠️ CLASSES WITH LOWEST ACCURACY (Bottom 10) |
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================================================================================ |
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class_name correct_count wrong_count total_count accuracy |
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pins_Taylor Swift 129 1 130 0.992308 |
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pins_elizabeth olsen 219 2 221 0.990950 |
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pins_Brenton Thwaites 207 2 209 0.990431 |
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pins_Emilia Clarke 207 2 209 0.990431 |
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pins_scarlett johansson 199 2 201 0.990050 |
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pins_Tom Holland 187 2 189 0.989418 |
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pins_Logan Lerman 209 3 212 0.985849 |
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pins_Zendaya 135 2 137 0.985401 |
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pins_Jessica Barden 138 3 141 0.978723 |
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pins_Brie Larson 165 4 169 0.976331 |
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================================================================================ |
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✅ CORRECT PREDICTIONS SAMPLE (showing 10 of 17442) |
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================================================================================ |
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✓ Adriana Lima0_0.jpg: pins_Adriana Lima (confidence: 0.787) |
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✓ Adriana Lima101_3.jpg: pins_Adriana Lima (confidence: 0.946) |
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✓ Adriana Lima102_4.jpg: pins_Adriana Lima (confidence: 0.907) |
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✓ Adriana Lima103_5.jpg: pins_Adriana Lima (confidence: 0.752) |
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✓ Adriana Lima104_6.jpg: pins_Adriana Lima (confidence: 0.886) |
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✓ Adriana Lima105_7.jpg: pins_Adriana Lima (confidence: 0.779) |
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✓ Adriana Lima106_8.jpg: pins_Adriana Lima (confidence: 0.794) |
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✓ Adriana Lima107_9.jpg: pins_Adriana Lima (confidence: 0.930) |
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✓ Adriana Lima108_10.jpg: pins_Adriana Lima (confidence: 0.902) |
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✓ Adriana Lima109_11.jpg: pins_Adriana Lima (confidence: 0.375) |
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================================================================================ |
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📁 OUTPUT FILES SAVED: |
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================================================================================ |
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✅ predictions_results.csv |
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→ All predictions sorted (correct first, then wrong) |
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→ Columns: filename, actual, predicted, confidence, status, top3 |
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✅ predictions_summary.csv |
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→ Per-class accuracy summary |
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→ Columns: class_name, correct_count, wrong_count, total_count, accuracy |
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================================================================================ |
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================================================================================ |
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❌ FAILED TO PROCESS (48 images) |
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================================================================================ |
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• Anne Hathaway203_391.jpg: No face detected |
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• Avril Lavigne11_572.jpg: No face detected |
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• Avril Lavigne174_619.jpg: No face detected |
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• Avril Lavigne41_685.jpg: No face detected |
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• barbara palvin158_800.jpg: No face detected |
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• Cristiano Ronaldo209_1326.jpg: No face detected |
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• Cristiano Ronaldo226_1333.jpg: No face detected |
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• Eliza Taylor202_775.jpg: No face detected |
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• Elizabeth Lail102_1055.jpg: No face detected |
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• Elizabeth Lail102_1056.jpg: No face detected |
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• Elizabeth Lail194_1117.jpg: No face detected |
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• Emilia Clarke78_1050.jpg: No face detected |
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• Emma Stone73_1817.jpg: No face detected |
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• Hugh Jackman119_1289.jpg: No face detected |
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• jeff bezos112_2049.jpg: No face detected |
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• jeff bezos12_2052.jpg: No face detected |
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• jeff bezos160_2068.jpg: No face detected |
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• jeff bezos178_2073.jpg: No face detected |
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• Jeremy Renner175_2634.jpg: No face detected |
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• Johnny Depp23_1863.jpg: No face detected |
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... and 28 more |
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✅ Failed images list saved to: failed_predictions.csv |
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================================================================================ |
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✅ PROCESSING COMPLETE! |
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================================================================================ |
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""" |
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st.set_page_config( |
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page_title="Face Recognition System", |
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layout="wide", |
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) |
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st.markdown( |
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""" |
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<style> |
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html, body, .stApp { |
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background-color: #ffffff !important; |
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color: #000000 !important; |
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} |
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[data-testid="stAppViewContainer"], |
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[data-testid="stAppViewContainer"] > .main, |
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.block-container { |
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background-color: #ffffff !important; |
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box-shadow: none !important; |
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} |
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[data-testid="stSidebar"] { |
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background-color: #ffffff !important; |
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border-right: 1px solid #e5e7eb !important; |
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} |
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header[data-testid="stHeader"] { |
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visibility: hidden; |
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height: 0px; |
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} |
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:root { |
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--primary-color: #2563eb; |
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--primary-hover: #1d4ed8; |
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--text-main: #0f172a; |
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--text-muted: #64748b; |
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} |
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.app-title h2 { |
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color: var(--text-main); |
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font-weight: 700; |
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margin-bottom: 0.4rem; |
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} |
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.stButton>button { |
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border-radius: 999px !important; |
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border: none !important; |
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background-color: var(--primary-color) !important; |
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color: #ffffff !important; |
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padding: 0.30rem 0.95rem !important; |
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font-weight: 500 !important; |
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font-size: 0.85rem !important; |
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white-space: nowrap !important; |
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transition: none !important; /* avoid hover jiggle */ |
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} |
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.stButton>button:hover { |
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background-color: var(--primary-hover) !important; |
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color: #ffffff !important; |
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} |
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.nav-wrap { |
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height: 56px; /* fixed height to prevent bouncing */ |
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display: flex; |
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justify-content: flex-end; |
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align-items: center; |
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gap: 0.6rem; |
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margin-top: 0.2rem; |
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margin-bottom: 0.4rem; |
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} |
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.stAlert { |
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border-radius: 0.75rem; |
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} |
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</style> |
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""", |
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unsafe_allow_html=True, |
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) |
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@st.cache_resource |
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def init_models(): |
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load_models() |
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return True |
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_ = init_models() |
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if "page" not in st.session_state: |
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st.session_state["page"] = "Home" |
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nav_row = st.container() |
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with nav_row: |
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spacer_col, nav_col = st.columns([2, 3]) |
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with nav_col: |
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st.markdown('<div class="nav-wrap">', unsafe_allow_html=True) |
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col_home, col_train, col_cm, col_pred, col_about = st.columns( |
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[1.0, 1.9, 1.4, 2.0, 1.1] |
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) |
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with col_home: |
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if st.button("Home"): |
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st.session_state["page"] = "Home" |
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with col_train: |
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if st.button("Training Report"): |
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st.session_state["page"] = "Training Report" |
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with col_cm: |
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if st.button("C_Matrix"): |
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st.session_state["page"] = "C_Matrix" |
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with col_pred: |
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if st.button("Prediction Report"): |
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st.session_state["page"] = "Prediction Report" |
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with col_about: |
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if st.button("About"): |
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st.session_state["page"] = "About" |
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st.markdown("</div>", unsafe_allow_html=True) |
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title_row = st.container() |
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with title_row: |
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st.markdown( |
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""" |
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<div class="app-title"> |
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<h2>Face Recognition System</h2> |
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</div> |
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""", |
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unsafe_allow_html=True, |
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) |
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st.markdown("---") |
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page = st.session_state["page"] |
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if page == "Home": |
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st.markdown("### 🔍 Face Recognition Demo") |
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st.write( |
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"Use the controls on the left to select an image from the dataset and run prediction." |
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) |
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st.sidebar.header("Image Selection") |
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if not DATA_ROOT.exists(): |
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st.sidebar.error( |
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f"Dataset folder not found at:\n`{DATA_ROOT}`\n\n" |
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"Make sure the Hugging Face dataset repo is added as a folder in this Space." |
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) |
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st.warning("Dataset not ready yet.") |
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selected_image_path = None |
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else: |
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person_folders = sorted([f for f in DATA_ROOT.iterdir() if f.is_dir()]) |
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if not person_folders: |
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st.sidebar.warning("No class folders found inside the dataset directory.") |
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selected_image_path = None |
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else: |
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person_names = [folder.name for folder in person_folders] |
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person_option = st.sidebar.selectbox( |
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"Select Person Folder", ["All"] + person_names |
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) |
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if person_option == "All": |
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image_files = [ |
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p |
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for p in DATA_ROOT.rglob("*") |
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if p.is_file() and p.suffix.lower() in IMAGE_EXTENSIONS |
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] |
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else: |
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selected_person_path = DATA_ROOT / person_option |
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image_files = [ |
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p |
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for p in selected_person_path.iterdir() |
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if p.is_file() and p.suffix.lower() in IMAGE_EXTENSIONS |
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|
] |
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if not image_files: |
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st.sidebar.warning("No images found in the selected folder.") |
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selected_image_path = None |
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else: |
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display_names = [ |
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img.relative_to(DATA_ROOT).as_posix() for img in image_files |
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] |
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|
selected_display = st.sidebar.selectbox("Select Image", display_names) |
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|
selected_image_path = DATA_ROOT / selected_display |
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if selected_image_path is None: |
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|
st.info("Dataset not ready or no image selected yet.") |
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|
else: |
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|
st.sidebar.image( |
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|
selected_image_path.as_posix(), |
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|
caption="Selected Image", |
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|
use_container_width=True, |
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|
) |
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col1, col2 = st.columns([1, 1]) |
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|
with col1: |
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st.subheader("Chosen Image") |
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|
st.image(selected_image_path.as_posix(), use_container_width=True) |
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with col2: |
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st.subheader("Prediction Output") |
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|
if st.button("Predict"): |
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|
result = predict_image(selected_image_path.as_posix()) |
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|
if result.get("error"): |
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|
st.error(result["error"]) |
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|
else: |
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|
st.success("Prediction complete") |
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|
st.write("##### Predicted Label") |
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|
st.write(f"**{result['predicted_label']}**") |
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|
st.write("##### Confidence") |
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|
st.write(f"**{result['confidence']:.4f}**") |
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|
else: |
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|
st.info("Click **Predict** to run model inference.") |
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|
elif page == "Training Report": |
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|
st.markdown("### Training Analysis Report") |
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|
st.write("Classification metrics and evaluation summary from your notebook:") |
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|
st.code(TRAINING_REPORT_TEXT, language="text") |
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|
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|
elif page == "C_Matrix": |
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|
st.markdown("### Confusion Matrix (subset evaluation)") |
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|
st.write( |
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|
"Programmatically computed confusion matrix on a small subset of the dataset." |
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|
) |
|
|
|
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|
with st.spinner("Computing confusion matrix on a subset of the dataset..."): |
|
|
df, acc = evaluate_dataset(images_per_class=2, max_images=150) |
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|
|
|
if df is None or df.empty: |
|
|
st.info("Could not compute confusion matrix: evaluation subset is empty.") |
|
|
else: |
|
|
labels = sorted(df["true_label"].unique()) |
|
|
cm = confusion_matrix(df["true_label"], df["predicted_label"], labels=labels) |
|
|
|
|
|
fig, ax = plt.subplots(figsize=(8, 8)) |
|
|
im = ax.imshow(cm, interpolation="nearest") |
|
|
ax.set_xticks(np.arange(len(labels))) |
|
|
ax.set_yticks(np.arange(len(labels))) |
|
|
ax.set_xticklabels(labels, rotation=90, fontsize=5) |
|
|
ax.set_yticklabels(labels, fontsize=5) |
|
|
ax.set_xlabel("Predicted label") |
|
|
ax.set_ylabel("True label") |
|
|
ax.set_title("Confusion Matrix (subset)") |
|
|
plt.tight_layout() |
|
|
|
|
|
st.pyplot(fig) |
|
|
|
|
|
if acc is not None: |
|
|
st.caption(f"Subset accuracy on this evaluation run: {acc:.4f}") |
|
|
|
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|
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|
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|
|
|
elif page == "Prediction Report": |
|
|
st.markdown("### Prediction Report") |
|
|
st.write("Full prediction run analysis and per-class performance:") |
|
|
st.code(PREDICTION_REPORT_TEXT, language="text") |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
elif page == "About": |
|
|
st.markdown("### About This Project") |
|
|
|
|
|
readme_path = PROJECT_ROOT / "README.md" |
|
|
if readme_path.exists(): |
|
|
readme_text = readme_path.read_text(encoding="utf-8") |
|
|
st.markdown(readme_text) |
|
|
else: |
|
|
st.write( |
|
|
""" |
|
|
This app demonstrates a deployable face recognition system |
|
|
built with a FaceNet backbone (InceptionResnetV1) and an SVM classifier |
|
|
trained on the `face_recognition_dataset` (Pins celebrities). |
|
|
""" |
|
|
) |
|
|
|
|
|
st.markdown("---") |
|
|
st.write("Developed by **Mr.Karan**") |
|
|
|
