app.py

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()