File size: 3,099 Bytes
fad4787 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | import gradio as gr
import cv2
import numpy as np
from PIL import Image
import os
from utils.model_loader import load_blood_group_model, load_gender_model
from utils.predictor import predict_blood_group, predict_gender
from utils.preprocessing import preprocess_fingerprint
# Load models on startup
blood_model = load_blood_group_model()
gender_model = load_gender_model()
def analyze_fingerprint(image):
"""Analyze uploaded fingerprint image"""
if image is None:
return "Please upload a fingerprint image.", "", "", ""
try:
# Preprocess for both models
preprocessed_blood = preprocess_fingerprint(image, model_type="blood_group")
preprocessed_gender = preprocess_fingerprint(image, model_type="gender")
# Get predictions
blood_result = predict_blood_group(blood_model, preprocessed_blood)
gender_result = predict_gender(gender_model, preprocessed_gender)
# Format results
if blood_result["success"]:
blood_text = f"**Predicted Blood Group:** {blood_result['blood_group']}\n**Confidence:** {blood_result['confidence']:.2%}"
blood_details = "\n".join([f"{k}: {v:.2%}" for k, v in blood_result['all_scores'].items()])
else:
blood_text = "Error predicting blood group"
blood_details = str(blood_result.get('error', 'Unknown error'))
if gender_result["success"]:
gender_text = f"**Predicted Gender:** {gender_result['gender']}\n**Confidence:** {gender_result['confidence']:.2%}"
else:
gender_text = "Error predicting gender"
return blood_text, blood_details, gender_text, ""
except Exception as e:
return f"Error processing image: {str(e)}", "", "", ""
# Create Gradio interface
with gr.Blocks(title="Forensic Fingerprint Analysis", theme=gr.themes.Soft()) as demo:
gr.Markdown("# 🔍 Forensic Fingerprint Analysis System")
gr.Markdown("Upload a fingerprint image to predict blood group and gender using machine learning models.")
with gr.Row():
with gr.Column():
image_input = gr.Image(label="Upload Fingerprint Image", type="pil")
analyze_btn = gr.Button("Analyze Fingerprint", variant="primary")
with gr.Column():
gr.Markdown("### Results")
blood_output = gr.Textbox(label="Blood Group Prediction", lines=2)
blood_scores = gr.Textbox(label="All Blood Group Scores", lines=8)
gender_output = gr.Textbox(label="Gender Prediction", lines=2)
# Connect the function
analyze_btn.click(
analyze_fingerprint,
inputs=image_input,
outputs=[blood_output, blood_scores, gender_output, gr.Textbox(visible=False)]
)
gr.Markdown("---")
gr.Markdown("**About:** 4th Year Forensic Research Project - Analyzing fingerprint patterns for biometric identification.")
# Launch the app
if __name__ == "__main__":
demo.launch()
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