File size: 3,132 Bytes
62bd7f8 |
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 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 |
import gradio as gr
import numpy as np
import cv2
import tempfile
import os
import sys
# Add project root to Python path
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
# Import predictor class
from src.predict_fracture import FracturePredictor
# Get current script location
current_dir = os.path.dirname(os.path.abspath(__file__))
project_root = os.path.dirname(current_dir) # Go up from app/ to project root
# CORRECTED MODEL PATHS
MODEL_PATH = 'models/fracture_detection_model.joblib'
ENCODER_PATH = 'models/label_encoder.joblib'
# Debugging output
print(f"Project root: {project_root}")
print(f"Model path: {MODEL_PATH}")
print(f"Model exists: {os.path.exists(MODEL_PATH)}")
print(f"Encoder exists: {os.path.exists(ENCODER_PATH)}")
# Initialize predictor only if files exist
if os.path.exists(MODEL_PATH) and os.path.exists(ENCODER_PATH):
predictor = FracturePredictor(model_path=MODEL_PATH, encoder_path=ENCODER_PATH)
else:
print("ERROR: Model files not found. Please run training first.")
exit(1)
def predict_fracture(img):
"""Process uploaded image and return prediction results"""
try:
# Handle different input types
if isinstance(img, np.ndarray):
# Convert to BGR format for OpenCV
if img.shape[2] == 4: # RGBA image
img_bgr = cv2.cvtColor(img, cv2.COLOR_RGBA2BGR)
else: # RGB image
img_bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
# Save to temp file
with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as tmp:
tmp_path = tmp.name
cv2.imwrite(tmp_path, img_bgr)
else:
# Already a file path
tmp_path = img
# Get prediction
label, confidence, vis_path = predictor.predict(tmp_path)
# Read visualization image
vis_img = cv2.imread(vis_path)
if vis_img is not None:
vis_img = cv2.cvtColor(vis_img, cv2.COLOR_BGR2RGB)
# Clean up temporary file
if isinstance(img, np.ndarray) and os.path.exists(tmp_path):
os.unlink(tmp_path)
return label, f"{confidence:.4f}", vis_img
except Exception as e:
print(f"Prediction error: {str(e)}")
return "Error", "N/A", None
# Create Gradio interface
iface = gr.Interface(
fn=predict_fracture,
inputs=gr.Image(label="Upload X-Ray Image"),
outputs=[
gr.Label(label="Prediction Result"),
gr.Textbox(label="Confidence Score"),
gr.Image(label="Prediction Visualization")
],
title="🦴 Bone Fracture Detection System",
description="Upload an X-ray image to detect bone fractures using GLCM features and SVM classifier",
examples=[
[os.path.join("samples", "fractured_1.jpg")],
[os.path.join("samples", "normal_1.jpg")]
],
flagging_mode="never"
)
if __name__ == "__main__":
iface.launch(server_name="0.0.0.0", server_port=7860)
|