model4 init

efficientnetb3-Plant-leaves-merge-v2-0.96.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d9bc8334814370c0ee60ce848e2d9c64adc3f59a15fe7b294c06ec217200fd73
+size 134980088

main.py

@@ -52,6 +52,8 @@ model_27 = load_model("efficientnetb3-Botanical-Garden-v1-98.88.h5")
 
 model_plant_leaves = load_model("efficientnetb3-Plant-leaves-v1-0.97.h5")
 
+model_merge_plant_leaves = load_model("efficientnetb3-Plant-leaves-merge-v2-0.96.h5")
+
 # Define class labels
 class_labels = {
     0: "Apple___Apple_scab",
@@ -149,6 +151,35 @@ class_plant_leaves = {
     21: "Pongamia Pinnata healthy (P7a)",
 }
 
+class_plant_leaves_merged = {
+    0: "Kola Jamun",
+    1: "Alstonia Scholaris healthy (P2b)",
+    2: "Pomegranate healthy (P9a)",
+    3: "Pongamia Pinnata healthy (P7a)",
+    4: "Jamun diseased (P5b)",
+    5: "Gauva healthy (P3a)",
+    6: "Shewali",
+    7: "Mango healthy (P0a)",
+    8: "Bael diseased (P4b)",
+    9: "Alstonia Scholaris diseased (P2a)",
+    10: "Pomegranate diseased (P9b)",
+    11: "Jatropha healthy (P6a)",
+    12: "Jamun healthy (P5a)",
+    13: "Gauva diseased (P3b)",
+    14: "Chinar healthy (P11a)",
+    15: "Lemon diseased (P10b)",
+    16: "Arjun diseased (P1a)",
+    17: "Pongamia Pinnata diseased (P7b)",
+    18: "Curry Patta",
+    19: "Basil healthy (P8)",
+    20: "Lemon healthy (P10a)",
+    21: "Jatropha diseased (P6b)",
+    22: "Arjun healthy (P1b)",
+    23: "Lemon",
+    24: "Chinar diseased (P11b)",
+    25: "Mango diseased (P0b)",
+}
+
 
 # Define a route to accept single image uploads and make predictions
 @app.post("/predict/")
@@ -228,10 +259,12 @@ async def predict_single_image_self(file: UploadFile):
     except Exception as e:
         return {"error": str(e)}
 
+
 def h():
     return "Hello World"
 
-@app.post("/predict-plant-dataset-22/")
+
+@app.post("/predict-plant-dataset-merge-25/")
 async def predict_single_image_self(file: UploadFile):
     try:
         # Read the uploaded image
@@ -262,19 +295,66 @@ async def predict_single_image_self(file: UploadFile):
         if not matched_rows.empty:
             matched_rows_json = matched_rows.to_json(orient="records")
 
-    
             result = {
                 "predicted_class": predicted_class_label,
                 "class_probabilities": predictions.tolist(),
                 "details": matched_rows_json,
             }
         else:
-            
             result = {
-            "predicted_class": predicted_class_label,
-            "class_probabilities": predictions.tolist(),
+                "predicted_class": predicted_class_label,
+                "class_probabilities": predictions.tolist(),
             }
 
+        return result
+
+    except Exception as e:
+        return {"error": str(e)}
+
+
+@app.post("/predict-plant-dataset-22/")
+async def predict_single_image_self(file: UploadFile):
+    try:
+        # Read the uploaded image
+        image = await file.read()
+        img = Image.open(io.BytesIO(image))
+
+        # Model Params
+        img_size = (224, 224)
+        channels = 3  # either BGR or Grayscale
+        color = "rgb"
+        img_shape = (img_size[0], img_size[1], channels)
+
+        # Preprocess the image to match the input requirements of your model
+        img = img.resize((224, 224))  # Adjust the size as needed
+        img = np.asarray(img)
+        img = preprocess_input(img)
+
+        # Make a prediction using your pre-trained model
+        predictions = model_merge_plant_leaves.predict(np.expand_dims(img, axis=0))
+
+        # Convert prediction indices to class labels
+        predicted_class_index = np.argmax(predictions)
+        predicted_class_label = class_plant_leaves_merged.get(
+            predicted_class_index, "Unknown"
+        )
+
+        matched_rows = df[df["id"] == predicted_class_index]
+        matched_rows_json = matched_rows.to_json(orient="records")
+
+        if not matched_rows.empty:
+            matched_rows_json = matched_rows.to_json(orient="records")
+
+            result = {
+                "predicted_class": predicted_class_label,
+                "class_probabilities": predictions.tolist(),
+                "details": matched_rows_json,
+            }
+        else:
+            result = {
+                "predicted_class": predicted_class_label,
+                "class_probabilities": predictions.tolist(),
+            }
 
         return result