Release KrishokChat backend ML suite: YOLO26 vision models and 5-fold soil moisture regression
12631d6 verified | #!/usr/bin/env python3 | |
| """KrishokChat ML Suite — Standalone Inference Runner for Academic Reviewers. | |
| Usage: | |
| # 1. Automatic Crop Identification | |
| python run_inference.py --crop crop --image sample_images/crop_sample.jpg | |
| # 2. Crop-specific Pathology Diagnosis | |
| python run_inference.py --crop potato --image sample_images/potato_sample.jpg | |
| python run_inference.py --crop rice --image sample_images/rice_sample.jpg | |
| python run_inference.py --crop wheat --image sample_images/wheat_sample.jpg | |
| python run_inference.py --crop corn --image sample_images/corn_sample.jpg | |
| python run_inference.py --crop chilli --image sample_images/chilli_sample.jpg | |
| python run_inference.py --crop brassica --image sample_images/brassica_sample.jpg | |
| # 3. Soil Moisture Tension (kPa) Prediction | |
| python run_inference.py --task soil --image sample_images/crop_sample.jpg | |
| """ | |
| import argparse | |
| import json | |
| import sys | |
| from pathlib import Path | |
| from PIL import Image | |
| import numpy as np | |
| try: | |
| import torch | |
| from ultralytics import YOLO | |
| except ImportError as e: | |
| print(f"Error: Required libraries missing. Run: pip install -r requirements.txt\n{e}") | |
| sys.exit(1) | |
| BASE_DIR = Path(__file__).resolve().parent | |
| CROP_MAP = { | |
| "crop": ("01_crop_classifier", "crop_classifier.pt"), | |
| "intake": ("01_crop_classifier", "crop_classifier.pt"), | |
| "potato": ("02_potato_disease", "potato_disease.pt"), | |
| "rice": ("03_rice_disease", "rice_disease.pt"), | |
| "wheat": ("04_wheat_disease", "wheat_disease.pt"), | |
| "corn": ("05_corn_disease", "corn_disease.pt"), | |
| "maize": ("05_corn_disease", "corn_disease.pt"), | |
| "chilli": ("06_chilli_disease", "chilli_disease.pt"), | |
| "chili": ("06_chilli_disease", "chilli_disease.pt"), | |
| "brassica": ("07_brassica_disease", "brassica_disease.pt"), | |
| "cabbage": ("07_brassica_disease", "brassica_disease.pt"), | |
| "cauliflower": ("07_brassica_disease", "brassica_disease.pt"), | |
| } | |
| def run_vision(crop: str, image_path: Path, topk: int = 3): | |
| folder, model_file = CROP_MAP[crop.lower()] | |
| model_path = BASE_DIR / folder / model_file | |
| classes_path = BASE_DIR / folder / "class_names.json" | |
| details_path = BASE_DIR / folder / "disease_details.json" | |
| print(f"Loading YOLO26 model: {model_path.relative_to(BASE_DIR)} ...") | |
| model = YOLO(str(model_path)) | |
| with open(classes_path, encoding="utf-8") as f: | |
| classes = json.load(f) | |
| if isinstance(classes, dict): | |
| class_names = [classes[str(i)] for i in range(len(classes))] | |
| else: | |
| class_names = list(classes) | |
| print(f"Processing image: {image_path.name} ...") | |
| img = Image.open(image_path).convert("RGB") | |
| results = model.predict(img, imgsz=224, verbose=False)[0] | |
| probs = results.probs.data.cpu().numpy() | |
| top_indices = np.argsort(probs)[::-1][:topk] | |
| print("\n" + "=" * 60) | |
| print(f" DIAGNOSTIC RESULTS ({crop.upper()} - YOLO26)") | |
| print("=" * 60) | |
| for rank, idx in enumerate(top_indices, 1): | |
| c_name = class_names[idx] if idx < len(class_names) else str(idx) | |
| conf = float(probs[idx]) | |
| print(f" {rank}. {c_name:<35} | Confidence: {conf:6.2%}") | |
| print("-" * 60) | |
| # Print agronomic advice if disease details exist | |
| if details_path.exists(): | |
| top_label = class_names[top_indices[0]] | |
| try: | |
| with open(details_path, encoding="utf-8") as f: | |
| details = json.load(f) | |
| # Find matching advice section | |
| advice_found = False | |
| for sec_key, sec_val in details.items(): | |
| if isinstance(sec_val, dict) and "classes" in sec_val: | |
| for entry in sec_val["classes"]: | |
| if entry.get("name") == top_label or top_label.endswith(entry.get("name", "___")): | |
| print("\nAGRONOMIC MANAGEMENT ADVICE:") | |
| if "bangla_name" in entry: | |
| print(f" • রোগ (বাংলা): {entry['bangla_name']}") | |
| if "symptoms" in entry: | |
| print(f" • Symptoms: {entry['symptoms']}") | |
| if "organic_control" in entry: | |
| print(f" • জৈব প্রতিকার: {entry['organic_control']}") | |
| if "chemical_control" in entry: | |
| print(f" • রাসায়নিক নিয়ন্ত্রণ: {entry['chemical_control']}") | |
| advice_found = True | |
| break | |
| if advice_found: | |
| break | |
| except Exception: | |
| pass | |
| def run_soil(image_path: Path): | |
| soil_dir = BASE_DIR / "08_soil_moisture" | |
| sys.path.insert(0, str(soil_dir)) | |
| from model_def import load_soil_model | |
| import torchvision.transforms as T | |
| transform = T.Compose([ | |
| T.Resize((224, 224)), | |
| T.ToTensor(), | |
| T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), | |
| ]) | |
| img = Image.open(image_path).convert("RGB") | |
| tensor = transform(img).unsqueeze(0) | |
| print("Running 5-Fold EfficientNet-B0 Ensemble for Soil Moisture Tension ...") | |
| preds = [] | |
| for fold in range(5): | |
| m = load_soil_model(soil_dir / f"effnetb0_fold{fold}.pt", device="cpu") | |
| with torch.no_grad(): | |
| val = m(tensor).item() | |
| preds.append(val) | |
| print(f" • Fold {fold}: {val:6.2f} kPa") | |
| mean_kpa = sum(preds) / len(preds) | |
| print("-" * 60) | |
| print(f" ENSEMBLE PREDICTED SOIL TENSION: {mean_kpa:6.2f} kPa") | |
| # Status | |
| if mean_kpa < 10.0: | |
| status = "Field Capacity / High Moisture (No irrigation needed)" | |
| elif mean_kpa < 30.0: | |
| status = "Optimal Moisture for Vegetative Growth" | |
| elif mean_kpa < 50.0: | |
| status = "Moderate Drying (Plan irrigation in 24-48 hrs)" | |
| else: | |
| status = "Moisture Deficit (Irrigate immediately)" | |
| print(f" STATUS: {status}") | |
| print("=" * 60) | |
| def main(): | |
| parser = argparse.ArgumentParser(description="KrishokChat ML Standalone Inference CLI") | |
| parser.add_argument("--crop", type=str, choices=list(CROP_MAP.keys()), help="Target crop or 'crop' for intake classifier") | |
| parser.add_argument("--task", type=str, choices=["vision", "soil"], default="vision", help="ML Task") | |
| parser.add_argument("--image", type=str, required=True, help="Path to input RGB image") | |
| parser.add_argument("--topk", type=int, default=3, help="Top-K classes to display") | |
| args = parser.parse_args() | |
| img_path = Path(args.image) | |
| if not img_path.exists(): | |
| print(f"Error: Image not found: {img_path}") | |
| sys.exit(1) | |
| if args.task == "soil": | |
| run_soil(img_path) | |
| else: | |
| if not args.crop: | |
| print("Error: Specify --crop <crop_name> (e.g. potato, rice, wheat, corn, chilli, brassica, crop)") | |
| sys.exit(1) | |
| run_vision(args.crop, img_path, args.topk) | |
| if __name__ == "__main__": | |
| main() | |