Create Inference.py

Inference.py

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+import gradio as gr
+from transformers import pipeline
+from PIL import Image
+
+# Load and return a dictionary of pipelines for reuse in other applications or scripts
+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)
+
+    # Combine predictions and generate advice
+    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
+    }
+
+# Example usage
+if __name__ == '__main__':
+    from PIL import Image
+    # Load sample 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'])