Delete app.py

app.py

@@ -1,33 +0,0 @@
-import torch
-import gradio as gr
-import numpy as np
-from config import MLP
-
-# Load model
-model = MLP()
-model.load_state_dict(torch.load("pytorch_model.pth", map_location=torch.device("cpu")))
-model.eval()
-
-# Example class names (you can change this)
-class_names = [f"Class {i}" for i in range(8)]
-
-# Prediction function
-def predict(input_vector):
-    input_array = np.array(input_vector).astype(np.float32)
-    if len(input_array) != 1000:
-        return "Error: Input must be 1000 numbers"
-    tensor = torch.tensor(input_array).unsqueeze(0)
-    with torch.no_grad():
-        output = model(tensor)
-        probs = torch.nn.functional.softmax(output[0], dim=0)
-    return {class_names[i]: float(probs[i]) for i in range(8)}
-
-# Gradio interface
-demo = gr.Interface(
-    fn=predict,
-    inputs=gr.Textbox(lines=5, placeholder="Enter 1000 comma-separated numbers..."),
-    outputs=gr.Label(num_top_classes=3),
-    title="MLP Vector Classifier"
-)
-
-demo.launch()