| import gradio as gr |
| from transformers import pipeline |
| from PIL import Image |
|
|
| |
| def load_pipeline_models(): |
| """ |
| Returns a dict of pretrained pipelines: |
| - 'disease': crop disease classification pipeline |
| - 'nutrient': nutrient deficiency classification pipeline |
| """ |
| disease_pipe = pipeline( |
| task="image-classification", |
| model="linkanjarad/mobilenet_v2_1.0_224-plant-disease-identification", |
| top_k=3 |
| ) |
| nutrient_pipe = pipeline( |
| task="image-classification", |
| model="nateraw/vit-base-beans", |
| top_k=3 |
| ) |
| return {"disease": disease_pipe, "nutrient": nutrient_pipe} |
|
|
|
|
| def diagnose(image: Image.Image, models=None): |
| """ |
| Runs inference on a PIL Image using both pipelines. |
| |
| Args: |
| image (PIL.Image): Input image of a crop leaf. |
| models (dict): Optional dict from load_pipeline_models(); if None, will be loaded. |
| |
| Returns: |
| dict: { |
| 'disease': [ {'label': str, 'score': float}, ... ], |
| 'nutrient': [ {'label': str, 'score': float}, ... ], |
| 'advice': list(str) |
| } |
| """ |
| if models is None: |
| models = load_pipeline_models() |
|
|
| disease_results = models["disease"](image) |
| nutrient_results = models["nutrient"](image) |
|
|
| |
| advice = [] |
| top_disease = disease_results[0]['label'].lower() |
| top_nutrient = nutrient_results[0]['label'].lower() |
|
|
| if "healthy" in top_disease: |
| advice.append("No disease detected—maintain standard crop care.") |
| else: |
| advice.append(f"Disease detected: {disease_results[0]['label']}. Isolate and apply targeted treatment.") |
|
|
| if "healthy" in top_nutrient: |
| advice.append("No nutrient deficiency detected—continue regular fertilization.") |
| else: |
| advice.append(f"Nutrient issue: {nutrient_results[0]['label']}. Amend soil based on deficiency (e.g., add N, P, or K).") |
|
|
| return { |
| 'disease': [ {'label': r['label'], 'score': r['score']} for r in disease_results ], |
| 'nutrient': [ {'label': r['label'], 'score': r['score']} for r in nutrient_results ], |
| 'advice': advice |
| } |
|
|
| |
| if __name__ == '__main__': |
| from PIL import Image |
| |
| img = Image.open('Plants/Unhealthy_crop_1.jpg') |
| models = load_pipeline_models() |
| result = diagnose(img, models=models) |
| print("Disease Predictions:", result['disease']) |
| print("Nutrient Predictions:", result['nutrient']) |
| print("Advice:", result['advice']) |
|
|
