|
import gradio as gr |
|
import matplotlib.pyplot as plt |
|
from PIL import Image |
|
from ultralytics import YOLO |
|
import cv2 |
|
import numpy as np |
|
import requests |
|
import json |
|
import os |
|
|
|
model = YOLO('Corn-Disease50Epoch.pt') |
|
name = ['Corn Rust', 'Leaf Blight', 'Gray Leaf Spot', 'Healthy'] |
|
image_directory = "/home/user/app/images" |
|
|
|
API_KEY = os.environ.get("API_KEY") |
|
|
|
def get_deepseek_solution(disease_name): |
|
try: |
|
response = requests.post( |
|
url="https://openrouter.ai/api/v1/chat/completions", |
|
headers={ |
|
"Authorization": f"Bearer {API_KEY}", |
|
"Content-Type": "application/json" |
|
}, |
|
data=json.dumps({ |
|
"model": "deepseek/deepseek-r1-distill-llama-70b:free", |
|
"messages": [ |
|
{ |
|
"role": "system", |
|
"content": "Anda adalah asisten yang hanya dapat memberikan jawaban berdasarkan materi yang diberikan." |
|
}, |
|
{ |
|
"role": "user", |
|
"content": f"Apa penyebab dan solusi penyakit jagung '{disease_name}'?" |
|
} |
|
] |
|
}) |
|
) |
|
|
|
if response.status_code == 200: |
|
result = response.json() |
|
return result.get("choices", [{}])[0].get("message", {}).get("content", "").strip() |
|
else: |
|
return "DeepSeek gagal memberikan jawaban (kode error: {}).".format(response.status_code) |
|
|
|
except requests.exceptions.RequestException as e: |
|
return f"Gagal terhubung ke DeepSeek: {e}" |
|
|
|
def response2(image, image_size=640, conf_threshold=0.3, iou_threshold=0.6): |
|
results = model.predict(image, conf=conf_threshold, iou=iou_threshold, imgsz=image_size) |
|
|
|
text = "" |
|
solution = "" |
|
detected_diseases = set() |
|
|
|
for r in results: |
|
im_array = r.plot() |
|
im = Image.fromarray(im_array[..., ::-1]) |
|
|
|
for r in results: |
|
conf = np.array(r.boxes.conf.cpu()) |
|
cls = np.array(r.boxes.cls.cpu()).astype(int) |
|
xywh = np.array(r.boxes.xywh.cpu()).astype(int) |
|
|
|
for con, cl, xy in zip(conf, cls, xywh): |
|
if cl < len(name): |
|
disease_name = name[cl] |
|
else: |
|
disease_name = "Unknown" |
|
|
|
confidence = round(float(con) * 100, 1) |
|
text += f"Detected {disease_name} with confidence {confidence}% at ({xy[0]},{xy[1]})\n" |
|
detected_diseases.add(disease_name) |
|
|
|
explanation_cache = {} |
|
|
|
for disease in detected_diseases: |
|
if disease.lower() == "healthy": |
|
solution += f"\n--- {disease} ---\nTanaman tampak sehat. Tidak ada tindakan diperlukan.\n" |
|
elif disease in name: |
|
if disease not in explanation_cache: |
|
explanation_cache[disease] = get_deepseek_solution(disease) |
|
solution += f"\n--- {disease} ---\n{explanation_cache[disease]}\n" |
|
else: |
|
solution += f"\n--- {disease} ---\nJenis penyakit tidak dikenali. Tidak dapat memberikan solusi.\n" |
|
|
|
return im, text.strip(), solution.strip() |
|
|
|
def pil_to_cv2(pil_image): |
|
return cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR) |
|
|
|
def process_video(video_path): |
|
cap = cv2.VideoCapture(video_path) |
|
while cap.isOpened(): |
|
ret, frame = cap.read() |
|
if not ret: |
|
break |
|
pil_img = Image.fromarray(frame[..., ::-1]) |
|
result = model.predict(source=pil_img) |
|
for r in result: |
|
im_array = r.plot() |
|
processed_frame = Image.fromarray(im_array[..., ::-1]) |
|
yield processed_frame |
|
cap.release() |
|
|
|
inputs = [ |
|
gr.Image(type="pil", label="Input Image"), |
|
gr.Slider(minimum=320, maximum=1280, value=640, step=32, label="Image Size"), |
|
gr.Slider(minimum=0.0, maximum=1.0, value=0.3, step=0.05, label="Confidence Threshold"), |
|
gr.Slider(minimum=0.0, maximum=1.0, value=0.6, step=0.05, label="IOU Threshold"), |
|
] |
|
|
|
outputs = [ |
|
gr.Image(type="pil", label="Output Image"), |
|
gr.Textbox(label="Result"), |
|
gr.Textbox(label="AI-Powered Solution") |
|
] |
|
|
|
examples = [ |
|
["/home/user/app/images/jagung7.jpg", 640, 0.3, 0.6], |
|
["/home/user/app/images/jagung4.jpeg", 640, 0.3, 0.6], |
|
["/home/user/app/images/jagung6.jpeg", 640, 0.3, 0.6] |
|
] |
|
|
|
title = """Corn Diseases Detection Finetuned YOLOv11 <br></br> <a href="https://colab.research.google.com/drive/1vnxtgPKOgfC8nyCL9hjrNFed75StsqGQ?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Colab" style="display:inline-block;"> </a> """ |
|
description = 'Image Size: Ukuran gambar untuk inferensi.\nConfidence Threshold: Minimum confidence untuk deteksi.\nIOU Threshold: Threshold untuk Non-Maximum Suppression (NMS).' |
|
|
|
video_iface = gr.Interface( |
|
fn=process_video, |
|
inputs=gr.Video(label="Upload Video", interactive=True), |
|
outputs=gr.Image(type="pil", label="Result"), |
|
title=title, |
|
description="Upload video untuk deteksi penyakit jagung." |
|
) |
|
|
|
image_iface = gr.Interface( |
|
fn=response2, |
|
inputs=inputs, |
|
outputs=outputs, |
|
examples=examples, |
|
title=title, |
|
description=description |
|
) |
|
|
|
demo = gr.TabbedInterface([image_iface, video_iface], ["Image Inference", "Video Inference"]) |
|
|
|
if __name__ == '__main__': |
|
demo.launch() |
|
|