Create app.py

app.py

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+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()