Delete app.py

app.py

@@ -1,123 +0,0 @@
-import gradio as gr
-import numpy as np
-import onnxruntime as ort
-import torch
-import gc
-import os
-import time
-
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from huggingface_hub import hf_hub_download, HfFolder
-
-token = HfFolder.get_token() or os.getenv("HF_TOKEN")
-
-HF_MODEL_ID = "mistralai/Mistral-Nemo-Instruct-2407"
-HF_ONNX_REPO = "techAInewb/mistral-nemo-2407-fp32"
-ONNX_MODEL_FILE = "model.onnx"
-
-# Shared tokenizer
-tokenizer = AutoTokenizer.from_pretrained(HF_MODEL_ID, token=token)
-
-def compare_outputs(prompt, show_tokens):
-    summary_log = []
-    pt_output_text = ""
-    ort_output_text = ""
-    pt_tokens = []
-    ort_tokens = []
-
-    try:
-        import psutil
-        ram_used = f"{psutil.virtual_memory().used / 1e9:.2f} GB"
-    except:
-        ram_used = "Unavailable"
-
-    # 🔹 PyTorch Generate
-    pt_start = time.time()
-    try:
-        torch_inputs = tokenizer(prompt, return_tensors="pt")
-        pt_model = AutoModelForCausalLM.from_pretrained(HF_MODEL_ID, torch_dtype=torch.float32, token=token)
-        pt_model.eval()
-        with torch.no_grad():
-            pt_outputs = pt_model.generate(**torch_inputs, max_new_tokens=50)
-        pt_output_ids = pt_outputs[0].tolist()
-        pt_output_text = tokenizer.decode(pt_output_ids, skip_special_tokens=True)
-        pt_tokens = tokenizer.convert_ids_to_tokens(pt_output_ids)
-        pt_time = time.time() - pt_start
-    finally:
-        del pt_model
-        gc.collect()
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-
-    # 🔹 ONNX Generate (Greedy)
-    ort_start = time.time()
-    ort_inputs = tokenizer(prompt, return_tensors="np")
-    onnx_path = hf_hub_download(repo_id=HF_ONNX_REPO, filename=ONNX_MODEL_FILE)
-    ort_session = ort.InferenceSession(onnx_path, providers=["CPUExecutionProvider"])
-    ort_output_ids = []
-    generated = ort_inputs["input_ids"]
-    attention_mask = ort_inputs["attention_mask"]
-    for _ in range(50):
-        ort_outputs = ort_session.run(None, {
-            "input_ids": generated,
-            "attention_mask": attention_mask
-        })
-        next_token_logits = ort_outputs[0][:, -1, :]
-        next_token = np.argmax(next_token_logits, axis=-1).reshape(-1, 1)
-        ort_output_ids.append(next_token[0][0])
-        generated = np.concatenate((generated, next_token), axis=1)
-        attention_mask = np.concatenate((attention_mask, np.ones((1, 1), dtype=np.int64)), axis=1)
-        if next_token[0][0] == tokenizer.eos_token_id:
-            break
-    ort_time = time.time() - ort_start
-    ort_tokens = tokenizer.convert_ids_to_tokens(ort_inputs["input_ids"][0].tolist() + ort_output_ids)
-    ort_output_text = tokenizer.decode(ort_inputs["input_ids"][0].tolist() + ort_output_ids, skip_special_tokens=True)
-
-    # 📊 Summary
-    summary_log.append("| Model   | Tokens | Time (s) | Time/Token |")
-    summary_log.append("|---------|--------|----------|------------|")
-    summary_log.append(f"| PyTorch | {len(pt_tokens)} | {pt_time:.2f} | {pt_time / max(1, len(pt_tokens)):.4f} |")
-    summary_log.append(f"| ONNX    | {len(ort_tokens)} | {ort_time:.2f} | {ort_time / max(1, len(ort_tokens)):.4f} |")
-    summary_log.append(f"\n📦 RAM Used: {ram_used}")
-    summary_log.append(f"📚 Tokenizer: {tokenizer.name_or_path} | Vocab size: {tokenizer.vocab_size}")
-    summary_log.append("🛠️ Note: This ONNX export is FP32. INT8 + Vitis AI variants coming soon.")
-
-    outputs = [pt_output_text, ort_output_text, "\n".join(summary_log)]
-
-    if show_tokens:
-        outputs += [
-            ", ".join(pt_tokens),
-            ", ".join(ort_tokens)
-        ]
-    else:
-        outputs += ["", ""]
-
-    return outputs
-
-example_prompts = [
-    "Who was the first president of the United States?",
-    "If you have 3 apples and eat 1, how many are left?",
-    "Write a short poem about memory and time.",
-    "Explain the laws of motion in simple terms.",
-    "What happens when you mix baking soda and vinegar?"
-]
-
-iface = gr.Interface(
-    fn=compare_outputs,
-    inputs=[
-        gr.Textbox(lines=2, placeholder="Enter a prompt..."),
-        gr.Checkbox(label="Show Token IDs")
-    ],
-    outputs=[
-        gr.Textbox(label="PyTorch Output"),
-        gr.Textbox(label="ONNX Output"),
-        gr.Textbox(label="Evaluation Summary"),
-        gr.Textbox(label="PyTorch Tokens"),
-        gr.Textbox(label="ONNX Tokens")
-    ],
-    title="ONNX vs PyTorch (Full Output + Token Trace)",
-    description="Run both models on your prompt and compare output text, timing, and token traces. Sequential model loading avoids OOM.",
-    examples=[[p, False] for p in example_prompts]
-)
-
-iface.launch()