Better

app.py

@@ -1,21 +1,15 @@
 import gradio as gr
 import pickle
-import numpy as np
 from PIL import Image
+
 from fastai.learner import load_learner
 
 model = load_learner('./mushrooms.pkl')
 
 # Function to preprocess image and classify
 def classify_image(image):
-    image = image.resize((224, 224))  # Resize if required by the model
-    image_array = np.array(image) / 255.0  # Normalize if needed
-    image_array = image_array.reshape(1, -1)  # Reshape if needed (depends on model)
-
-    # Run model prediction
-    prediction = model.predict(image_array)
-
-    return str(prediction[0])  # Return predicted class
+    prediction, _, probs = model.predict(image)
+    return str(prediction) + str(probs)  # Return predicted class
 
 # Gradio Interface
 iface = gr.Interface(

requirements.txt

@@ -1,4 +1,2 @@
 fastai
-torch
 gradio
-numpy