Update app.py

STILL RESOLVING THE OUTPUT ISSUE

app.py

@@ -1,21 +1,53 @@
 import gradio as gr
-from fastai.vision.all import load_learner
+from fastai.vision.all import load_learner, PILImage
 
-# Re-declare custom function from training (needed to load the model)
+# Re-declare custom function used during training (needed by load_learner)
 def is_cat(x): 
     return x[0].isupper()
 
-# Load your trained model
+# Load your exported learner
 learn = load_learner("model.pkl")
 
-# Prediction function
+# Build a reliable mapping from raw vocab entries -> friendly class names
+vocab = list(learn.dls.vocab)
+
+# Default mapping - try to detect boolean labels first
+class_name_map = {}
+if all(isinstance(v, bool) for v in vocab):
+    # Most likely True == Cat, False == Dog (this matches the fastbook pattern)
+    class_name_map = {True: "Cat 🐱", False: "Dog 🐶"}
+else:
+    # If vocab contains strings (or other values), map by content
+    for v in vocab:
+        s = str(v).lower()
+        if "cat" in s:
+            class_name_map[v] = "Cat 🐱"
+        elif "dog" in s:
+            class_name_map[v] = "Dog 🐶"
+        else:
+            # fallback to string version of the vocab entry
+            class_name_map[v] = str(v)
+
 def predict(img):
+    # Accept either an image file path or PIL image depending on Gradio input
+    if not isinstance(img, PILImage):
+        try:
+            img = PILImage.create(img)
+        except Exception:
+            pass
+
     pred, pred_idx, probs = learn.predict(img)
-    # Get the class name (instead of boolean)
-    label = learn.dls.vocab[pred_idx]
+
+    # raw label from the learner's vocab (might be bool)
+    raw_label = learn.dls.vocab[pred_idx]
+
+    # map to friendly string (fallback to str(raw_label))
+    label = class_name_map.get(raw_label, str(raw_label))
+
     confidence = float(probs[pred_idx]) * 100
+
     return f"This is a {label} with {confidence:.2f}% confidence."
 
-# Gradio app
-iface = gr.Interface(fn=predict, inputs="image", outputs="text")
+# Gradio UI (single-text output)
+iface = gr.Interface(fn=predict, inputs=gr.Image(type="pil"), outputs="text")
 iface.launch()