Update app.py

app.py

@@ -1,60 +1,65 @@
+import os
 import gradio as gr
 from transformers import pipeline
 from PIL import Image
 
-# Initialize the plant disease classification pipeline
-# You can replace the model with any fine-tuned plant disease model hosted on Hugging Face
+# 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="wambugu71/crop_leaf_diseases_vit",
+    model="yanyu/efficientformer_l1",
     top_k=3
 )
 
-
-def diagnose_plant_health(image: Image.Image):
+def diagnose_crop_health(image: Image.Image):
     """
-    Takes a PIL Image of a plant leaf and returns:
-      - Top predicted disease label
-      - Confidence score
-      - Care advice based on the label
+    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
     """
-    # Run the image through the classification pipeline
-    results = plant_disease_classifier(image)
+    disease_results = plant_disease_classifier(image)
+    nutrient_results = nutrient_deficiency_classifier(image)
 
-    # Format top-3 predictions
-    predictions = []
-    for res in results:
-        label = res['label']
-        score = res['score']
-        predictions.append(f"{label} ({score*100:.1f}%)")
+    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.")
 
-    # Determine advice based on the top prediction
-    top_label = results[0]['label'].lower()
-    if "healthy" in top_label:
-        advice = "Your plant looks healthy! Maintain regular watering and adequate sunlight."
+    if "healthy" in top_nutrient:
+        advices.append("No nutrient deficiency detected—continue regular fertilization.")
     else:
-        advice = (
-            f"Detected symptom: {results[0]['label']}. "
-            "Consider the following care steps:\n"
-            "1. Isolate the plant to prevent spread.\n"
-            "2. Prune affected areas with sterilized tools.\n"
-            "3. Apply an appropriate fungicide or treatment."
-        )
+        advices.append(f"Nutrient issue: {nutrient_results[0]['label']}. Amend soil based on deficiency (e.g., add N, P, or K).")
 
-    return "\n".join(predictions), advice
+    return "\n".join(disease_preds), "\n".join(nutrient_preds), "\n".join(advices)
 
-# Building the Gradio interface
+# Build the Gradio interface
 iface = gr.Interface(
-    fn=diagnose_plant_health,
-    inputs=gr.Image(type="pil", label="Upload Plant Leaf Image"),
+    fn=diagnose_crop_health,
+    inputs=gr.Image(type="pil", label="Upload Crop Leaf Image"),
     outputs=[
-        gr.Textbox(label="Predicted Diseases (Top 3)"),
+        gr.Textbox(label="Disease Predictions (Top 3)"),
+        gr.Textbox(label="Nutrient Predictions (Top 3)"),
         gr.Textbox(label="Care Advice")
     ],
-    title="Home Plant Health Monitor",
+    title="Crop Health Monitor",
     description=(
-        "Upload a photo of your plant's leaf to diagnose diseases and receive care recommendations. "
-        "This app uses a fine-tuned image-classification model on common plant diseases."
+        "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"],