Big model update

app.py

@@ -1,15 +1,38 @@
 import gradio as gr
+import pandas as pd
+import pathlib
+from fastbook import *
+from fastai.vision.widgets import *
+from fastai.vision.all import *
 
-image = gr.Image(type='pil')
-label = gr.Label()
 
-categories = ('A','B','C')
-probs = [0.3,0.3,0.3]
+# Load the model. "PosixPath" is something with windows/linux, I dont know really.
+temp = pathlib.PosixPath
+pathlib.PosixPath = pathlib.WindowsPath
+learn = load_learner('model.pkl') # Load the model itself
+pathlib.PosixPath = temp
+categories = learn.dls.vocab # Get the list of labels from the model
+
+# Load the list of trash-sorting, use the items as index.
+df_sort = pd.read_csv('Lista.csv',sep =";").set_index('Avfall')
 
 def classify_image(img):
-    # pred,idx,probs = learn.predict(img)
-    return dict(zip(categories,map(float,probs)))
+    # Make the prediction
+    trash,idx,probs = learn.predict(PILImage.create(img)) # Make prediction
+    df = pd.DataFrame()    # Create dataframe
+    df['categories'] = categories # Add categories to dataframe
+    df['probabilities'] = probs.numpy() # Add probabilities to dataframe
+    sorted_df = df.sort_values(by=['probabilities'], ascending=False).head() # Sort by probability, highest first, take the top 5
+    predictions = dict(zip(sorted_df['categories'].tolist(),map(float,sorted_df['probabilities'].tolist()))) # Now convert to a dictionary that we return later
+    
+    
+    # Create sorting statement
+    sort_text = "Sorteras som " + df_sort.loc[trash].tolist()[0]
+    return "Det där är...", predictions, sort_text # Return the dictionary
 
 
+image = gr.Image(type='pil')
+label = ["text",gr.Label(),"text"]
+
 iface = gr.Interface(fn=classify_image, inputs=image, outputs=label)
 iface.launch()