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72885c6 | 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 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 | """Gradio Blocks UI: image / video / webcam tabs plus a JSON API endpoint."""
import gradio as gr
from src.configs import settings
from src.inference.predictor import Predictor
from src.inference.video import annotate_video
def _predict_image(predictor: Predictor, image):
if image is None:
return None, "No image provided", "N/A"
result = predictor.classify(image)
return result.annotated_frame, result.label, result.confidence_str
def _predict_api(predictor: Predictor, image):
if image is None:
return {"success": False, "error": "No image provided"}
try:
result = predictor.classify(image)
return {
"success": True,
"class": result.label,
"confidence": result.confidence_str,
"confidence_value": result.confidence,
"threshold_exceeded": result.threshold_exceeded,
}
except Exception as exc:
return {"success": False, "error": str(exc)}
def _predict_video(predictor: Predictor, video_path):
if video_path is None:
return None
return annotate_video(video_path, predictor)
def build_interface(predictor: Predictor) -> gr.Blocks:
with gr.Blocks(title="SerraSafe - Détection de Pestes") as demo:
gr.Markdown(
f"""
# 🌱 SerraSafe Guardian - Système de Détection de Pestes
Ce système utilise l'intelligence artificielle pour détecter et classifier les pestes dans votre serre.
**Classes détectées:** {", ".join(settings.CLASS_LABELS)}
"""
)
with gr.Tab("📷 Analyse Image"):
with gr.Row():
with gr.Column():
image_input = gr.Image(type="numpy", label="Télécharger une image")
image_button = gr.Button("Analyser l'image", variant="primary")
with gr.Column():
image_output = gr.Image(label="Résultat")
image_label = gr.Textbox(label="Classe détectée")
image_confidence = gr.Textbox(label="Confiance")
image_button.click(
fn=lambda img: _predict_image(predictor, img),
inputs=image_input,
outputs=[image_output, image_label, image_confidence],
)
with gr.Tab("📹 Analyse Vidéo"):
with gr.Row():
with gr.Column():
video_input = gr.Video(label="Télécharger une vidéo")
video_button = gr.Button("Analyser la vidéo", variant="primary")
with gr.Column():
video_output = gr.Video(label="Vidéo annotée")
video_button.click(
fn=lambda path: _predict_video(predictor, path),
inputs=video_input,
outputs=video_output,
)
with gr.Tab("🎥 Webcam"):
gr.Markdown("### Capturez une image avec votre webcam pour l'analyser")
with gr.Row():
with gr.Column():
webcam_input = gr.Image(sources=["webcam"], type="numpy", label="Webcam", streaming=False)
webcam_button = gr.Button("Analyser", variant="primary")
with gr.Column():
webcam_output = gr.Image(label="Résultat")
webcam_label = gr.Textbox(label="Classe détectée")
webcam_confidence = gr.Textbox(label="Confiance")
webcam_button.click(
fn=lambda img: _predict_image(predictor, img),
inputs=webcam_input,
outputs=[webcam_output, webcam_label, webcam_confidence],
)
with gr.Tab("🔌 API (JSON)"):
gr.Markdown(
"""
### API pour clients externes
Utilisez cet endpoint pour obtenir des prédictions en JSON.
**Endpoint:** `/api/predict` (POST, retourne `class`, `confidence`, `threshold_exceeded`)
"""
)
with gr.Column():
api_image_input = gr.Image(type="numpy", label="Image")
api_button = gr.Button("Tester l'API", variant="primary")
api_output = gr.JSON(label="Réponse JSON")
api_button.click(
fn=lambda img: _predict_api(predictor, img),
inputs=api_image_input,
outputs=api_output,
api_name="predict",
)
return demo
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