app.py

import os
import gradio as gr
from transformers import pipeline
from PIL import Image

# Retrieve Hugging Face token from Secrets
hf_token = os.getenv("HF_TOKEN")

plant_disease_classifier = pipeline(
    task="image-classification",  
    model="linkanjarad/mobilenet_v2_1.0_224-plant-disease-identification",  
    top_k=3
)
nutrient_deficiency_classifier = pipeline(
    task="image-classification",
    model="yanyu/efficientformer_l1",
    top_k=3
)

def diagnose_crop_health(image: Image.Image):
    """
    Takes a PIL Image of a crop leaf and returns:
      - Top 3 disease predictions
      - Top 3 nutrient deficiency predictions
      - Combined care advice based on both
    """
    disease_results = plant_disease_classifier(image)
    nutrient_results = nutrient_deficiency_classifier(image)

    disease_preds = [f"{res['label']} ({res['score']*100:.1f}%)" for res in disease_results]
    nutrient_preds = [f"{res['label']} ({res['score']*100:.1f}%)" for res in nutrient_results]

    # Generate advice based on predictions
    advices = []
    top_disease = disease_results[0]['label'].lower()
    top_nutrient = nutrient_results[0]['label'].lower()

    if "healthy" in top_disease:
        advices.append("No disease detected—maintain standard crop care.")
    else:
        advices.append(f"Disease detected: {disease_results[0]['label']}. Isolate and apply targeted treatment.")

    if "healthy" in top_nutrient:
        advices.append("No nutrient deficiency detected—continue regular fertilization.")
    else:
        advices.append(f"Nutrient issue: {nutrient_results[0]['label']}. Amend soil based on deficiency (e.g., add N, P, or K).")

    return "\n".join(disease_preds), "\n".join(nutrient_preds), "\n".join(advices)

# Build the Gradio interface
iface = gr.Interface(
    fn=diagnose_crop_health,
    inputs=gr.Image(type="pil", label="Upload Crop Leaf Image"),
    outputs=[
        gr.Textbox(label="Disease Predictions (Top 3)"),
        gr.Textbox(label="Nutrient Predictions (Top 3)"),
        gr.Textbox(label="Care Advice")
    ],
    title="Crop Health Monitor",
    description=(
        "Upload a photo of your crop's leaf to diagnose diseases and nutrient deficiencies, "
        "and receive combined care recommendations using two complementary models."
    ),
    examples=[
        ["Plants/Unhealthy_crop_1.jpg"],
        ["Plants/Unhealthy_crop_2.jpg"],
        ["Plants/Unhealthy_crop_3.jpg"],
        ["Plants/Unhealthy_crop_4.jpg"],
        ["Plants/Unhealthy_crop_5.jpg"],
        ["Plants/Healthy_crop_1.jpg"],
        ["Plants/Healthy_crop_2.jpg"]
    ],
    allow_flagging="never"
)

if __name__ == "__main__":
    iface.launch(server_name="0.0.0.0", server_port=7860)