Delete app.py

app.py

@@ -1,41 +0,0 @@
-# app.py
-
-__all__ = ['bird', 'learn', 'classify_image', 'categories', 'image', 'label', 'examples', 'intf']
-
-# Load categories from labels.txt
-with open("labels.txt", "r") as file:
-    categories = [line.strip() for line in file.readlines()]
-
-# Import FastAI, PyTorch, and Gradio
-from fastai.vision.all import *
-import gradio as gr
-import torch
-from torchvision.models import resnet50
-
-# ✅ Define Model Architecture
-def create_model():
-    model = resnet50(weights=None)  # No pre-trained weights
-    num_features = model.fc.in_features  # Get input features of the final layer
-    model.fc = torch.nn.Linear(num_features, len(categories))  # Replace final layer
-    return model
-
-# ✅ Load Model Weights Safely
-model = create_model()
-model.load_state_dict(torch.load("model_weights.pth", map_location="cpu"))
-model.eval()  # Set to evaluation mode
-
-# ✅ Wrap in a FastAI Learner (Even Without DataLoaders)
-learn = Learner(dls=None, model=model)
-
-# ✅ Define the Classification Function
-def classify_image(img):
-    preds, idx, probs = learn.predict(img)
-    return dict(zip(categories, map(float, probs)))
-
-# ✅ Gradio UI Components
-image = gr.Image()
-label = gr.Label(num_top_classes=3)
-
-# ✅ Create and Launch Gradio Interface
-intf = gr.Interface(fn=classify_image, inputs=image, outputs=label)
-intf.launch(inline=False, share=True)