final_ver

app.py

@@ -2,24 +2,31 @@ import gradio as gr
 import torch
 import time
 from fastai.vision.all import load_learner
+from fastai.vision.all import *
 from PIL import Image
+from pathlib import Path
+import pathlib
+import PIL
 
 # Load the exported model
-model = load_learner("model.pkl")
+temp = pathlib.PosixPath
+pathlib.PosixPath = pathlib.WindowsPath
+model = load_learner(r"C:\Users\i0567479\Downloads\model.pkl")
 
 # Function to classify an image
 def classify_images(imgs):
+    [print(x) for x in imgs]
     start_time = time.time()
     results = []
     for img in imgs:
         # Convert gradio image to PIL Image
-        pil_img = Image.open(img.name)
+        #img = PILImage.create(img)
 
         # Perform inference
-        pred_class, pred_idx, pred_probs = model.predict(pil_img)
+        pred_class, pred_idx, pred_probs = model.predict(img)
 
         # Format output
-        output = f"Image Name: {img.name} - Category: {pred_class}"
+        output = f"Image Name: {Path(img).stem} - Category: {pred_class}"
         results.append(output)
 
     # Calculate total inference time
@@ -31,8 +38,8 @@ def classify_images(imgs):
     return results
 
 # Create Gradio interface
-input_component = gr.inputs.File(label="Upload Image", type="file", multiple_files=True)
-output_component = gr.outputs.Textbox(label="Classification Results")
+input_component = gr.File(label="Upload Image", file_count='multiple')
+output_component = gr.Textbox(label="Classification Results")
 interface = gr.Interface(fn=classify_images, inputs=input_component, outputs=output_component, title="Image Classifier")
 
 # Launch the Gradio interface