Update app.py

app.py

@@ -1,21 +1,28 @@
 import joblib
+import json
 import numpy as np
+import gradio as gr
 from tensorflow.keras.applications import MobileNetV2
 from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
 from tensorflow.keras.preprocessing import image
-import gradio as gr
-import json
+
+# --- Paths to your model and labels ---
+MODEL_PATH = "my_model_k7.pkl"
+LABELS_PATH = "labels.json"
 
 # --- Load model and labels ---
-model_data = joblib.load(MODEL_PATH)
-clf = model_data["model"]   # your KNN classifier
-labels = model_data["
-                    # the KNN classifier
-with open("labels.json", "r") as f:
+model_data = joblib.load(MODEL_PATH)      # this is a dict with model + labels
+clf = model_data["model"]                 # your KNN classifier
+labels = model_data.get("labels", None)   # try to load labels from inside dict
+
+# If labels are stored separately, override
+with open(LABELS_PATH, "r") as f:
     labels = json.load(f)
 
-# --- Feature extractor ---
-feature_extractor = MobileNetV2(weights="imagenet", include_top=False, pooling="avg")
+# --- Feature extractor (MobileNetV2 embeddings) ---
+feature_extractor = MobileNetV2(weights="imagenet",
+                                include_top=False,
+                                pooling="avg")
 
 def predict(img):
     # preprocess image
@@ -24,22 +31,22 @@ def predict(img):
     img = preprocess_input(img)
 
     # extract features
-    feat = feature_extractor.predict(img)
+    features = feature_extractor.predict(img)
 
-    # predict with KNN
-    pred_idx = int(clf.predict(feat)[0])
-    pred_label = labels[str(pred_idx)] if str(pred_idx) in labels else "Unknown"
+    # run through classifier
+    pred_idx = int(clf.predict(features)[0])
+    pred_label = labels[str(pred_idx)] if isinstance(labels, dict) else labels[pred_idx]
 
     return pred_label
 
 # --- Gradio UI ---
-demo = gr.Interface(
+iface = gr.Interface(
     fn=predict,
-    inputs=gr.Image(type="pil"),
-    outputs="text",
+    inputs=gr.Image(type="pil", label="Upload plant image"),
+    outputs=gr.Label(num_top_classes=1, label="Predicted class"),
     title="🌿 UAE Flora Classifier",
-    description="Upload a plant photo and the model will predict its species."
+    description="Upload a plant photo and I’ll predict the species using a KNN classifier over MobileNetV2 embeddings."
 )
 
 if __name__ == "__main__":
-    demo.launch()
+    iface.launch()