Update app.py

app.py

@@ -5,18 +5,33 @@ __all__ = ['learn', 'classify_image', 'bear', 'env', 'age', 'image', 'label', 'e
 from fastai.vision.all import *
 import gradio as gr
 
-def get_bear(p): 
-    name_components = str(p).split(' ')
-    return name_components[2]
+def get_x(r): return r['fname']
 
-def get_age(p):
-    name_components = str(p).split(' ')
-    return name_components[1]   
+def get_bear(r):
+    """Returns all bear types associated with the given filename."""
+    fname = r['fname']  # Extract filename from row
+    row = df[df['fname'] == fname]  # Filter dataframe using single value
+    if not row.empty:
+        bear_types = row['bear_type'].values[0].split()  # Get list of bear types
+        bear_types = [b.replace("bear", "").strip() for b in bear_types]  # Remove "bear" and trim spaces
+        return [b for b in bear_types if b]  # Remove any empty strings
+    return []
 
-def get_environ(p):
-    name_components = str(p).split(' ')
-    file_components = name_components[0].split('/')
-    return file_components[-1]
+def get_age(r):
+    """Returns all ages associated with the given filename."""
+    fname = r['fname']
+    row = df[df['fname'] == fname]
+    if not row.empty:
+        return row['age'].values[0].split()
+    return []
+
+def get_environ(r):
+    """Returns all environments associated with the given filename."""
+    fname = r['fname']
+    row = df[df['fname'] == fname]
+    if not row.empty:
+        return row['environ'].values[0].split()
+    return []
 
 def bear_err(inp,bear,environ,age): return error_rate(inp[:,:5],bear)
 def bear_loss(inp,bear,environ,age): return F.cross_entropy(inp[:,:5],bear)
@@ -28,7 +43,7 @@ def age_loss(inp,bear,environ,age): return F.cross_entropy(inp[:,8:],age)
 def combine_loss(inp,bear,environ,age):    
     return bear_loss(inp,bear,environ,age)+environ_loss(inp,bear,environ,age)+age_loss(inp,bear,environ,age)
 
-learn = load_learner('multitarget_bears.pkl')
+learn = load_learner('multimulti.pkl')
 
 bear = ('black', 'brown', 'grizzly', 'sloth', 'sun')
 env = ('forest', 'plains', 'water')