Update app.py

app.py

@@ -6,7 +6,7 @@ import cv2
 import json
 import os
 
-def analyse(img):
+def analyse(img, plant_type):
     # Load label_disease.json
     with open('data/label_disease.json', 'r') as f:
         label_disease = json.load(f)
@@ -33,16 +33,32 @@ def analyse(img):
     y_pred = dnn_model.predict(process_img)
     y_pred = y_pred[0]
 
-    # Identify overall prediction
+    # Identify plant-specific predictions
+    plant_label_ids = plant_label_disease[plant_type.lower()]
+    plant_predicted_id = plant_label_ids[0]
+    for disease in plant_label_ids:
+        if y_pred[disease] > y_pred[plant_predicted_id]:
+            plant_predicted_id = disease
+
+    # Determine overall prediction
     overall_predicted_id = int(np.argmax(y_pred))
     overall_predicted_name = label_disease[str(overall_predicted_id)]
     overall_predicted_confidence = float(y_pred[overall_predicted_id])
 
+    # Determine plant-specific prediction
+    plant_predicted_name = label_disease[str(plant_predicted_id)]
+    plant_predicted_confidence = float(y_pred[plant_predicted_id])
+
     # Determine health status
+    is_plant_specific_healthy = "healthy" in plant_predicted_name.lower()
     is_overall_healthy = "healthy" in overall_predicted_name.lower()
 
     # Return results as a JSON object
     result = {
+        "plant_specific_prediction_id": plant_predicted_id,
+        "plant_specific_prediction_name": plant_predicted_name,
+        "plant_specific_confidence": plant_predicted_confidence,
+        "is_plant_specific_healthy": is_plant_specific_healthy,
         "overall_prediction_id": overall_predicted_id,
         "overall_prediction_name": overall_predicted_name,
         "overall_confidence": overall_predicted_confidence,
@@ -54,11 +70,17 @@ def analyse(img):
 # Build the Gradio Blocks interface
 with gr.Blocks() as demo:
     gr.Markdown("## Plant Disease Detection")
-    gr.Markdown("Upload an image of a plant leaf to detect diseases.")
+    gr.Markdown("Upload an image of a plant leaf and select the plant type to detect diseases.")
     
     with gr.Row():
-        input_image = gr.Image(label="Upload Image", type="numpy")
-        submit = gr.Button("Analyze")
+        with gr.Column():
+            input_image = gr.Image(label="Upload Image", type="numpy")
+            plant_type = gr.Radio(
+                ["Apple", "Blueberry", "Cherry", "Corn", "Grape", "Orange", "Peach",
+                 "Pepper", "Potato", "Raspberry", "Soybean", "Squash", "Strawberry", "Tomato"],
+                label="Plant Type"
+            )
+            submit = gr.Button("Analyze")
             
         with gr.Column():
             result_json = gr.JSON(label="Analysis Result")
@@ -73,7 +95,7 @@ with gr.Blocks() as demo:
     )
     
     # Define interaction
-    submit.click(fn=analyse, inputs=[input_image], outputs=result_json)
+    submit.click(fn=analyse, inputs=[input_image, plant_type], outputs=result_json)
 
 # Launch the application
-demo.launch(share=True, show_error=True)
+demo.launch(share=True, show_error=True)