Delete app[1].py

app[1].py

@@ -1,51 +0,0 @@
-import gradio as gr
-import numpy as np
-import onnxruntime as ort
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from huggingface_hub import hf_hub_download
-import torch
-
-HF_MODEL_ID = "mistralai/Mistral-Nemo-Instruct-2407"
-HF_ONNX_REPO = "techAInewb/mistral-nemo-2407-fp32"
-ONNX_MODEL_FILE = "model.onnx"
-
-# Load tokenizer
-tokenizer = AutoTokenizer.from_pretrained(HF_MODEL_ID)
-
-# Load PyTorch model
-pt_model = AutoModelForCausalLM.from_pretrained(HF_MODEL_ID, torch_dtype=torch.float32)
-pt_model.eval()
-
-# Load ONNX model
-onnx_path = hf_hub_download(repo_id=HF_ONNX_REPO, filename=ONNX_MODEL_FILE)
-onnx_session = ort.InferenceSession(onnx_path, providers=["CPUExecutionProvider"])
-
-def compare_outputs(prompt):
-    inputs = tokenizer(prompt, return_tensors="np", padding=False)
-    torch_inputs = tokenizer(prompt, return_tensors="pt")
-
-    # Run PyTorch
-    with torch.no_grad():
-        pt_outputs = pt_model(**torch_inputs).logits
-    pt_top = torch.topk(pt_outputs[0, -1], 5).indices.tolist()
-
-    # Run ONNX
-    ort_outputs = onnx_session.run(None, {
-        "input_ids": inputs["input_ids"],
-        "attention_mask": inputs["attention_mask"]
-    })
-    ort_logits = ort_outputs[0]
-    ort_top = np.argsort(ort_logits[0, -1])[::-1][:5].tolist()
-
-    pt_tokens = tokenizer.convert_ids_to_tokens(pt_top)
-    ort_tokens = tokenizer.convert_ids_to_tokens(ort_top)
-
-    return f"PyTorch Top Tokens: {pt_tokens}", f"ONNX Top Tokens: {ort_tokens}"
-
-iface = gr.Interface(fn=compare_outputs,
-                     inputs=gr.Textbox(lines=2, placeholder="Enter a prompt..."),
-                     outputs=["text", "text"],
-                     title="ONNX vs PyTorch Model Comparison",
-                     description="Run both PyTorch and ONNX inference on a prompt and compare top predicted tokens.")
-
-iface.launch()