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e45b1b8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch.nn.functional as F
# Load model and tokenizer (same as the lesson)
CHECKPOINT = "distilbert-base-uncased-finetuned-sst-2-english"
tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT)
model = AutoModelForSequenceClassification.from_pretrained(CHECKPOINT)
def behind_the_pipeline(text):
# Step 1: Tokenization / Preprocessing
inputs = tokenizer(text, return_tensors="pt", truncation=True)
tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])
# Step 2: Model Inference (get logits)
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits[0]
# Step 3: Post-processing (softmax for probabilities)
probs = F.softmax(logits, dim=-1)
id2label = model.config.id2label
labels = [id2label[i] for i in range(len(probs))]
# Build results
tokenization_str = f"**Tokens:** {tokens}\n\n**Input IDs:** {inputs['input_ids'][0].tolist()}"
logits_str = f"**Logits (raw model output):**\n`{logits.tolist()}`"
probs_str = "\n".join([
f"- **{label}**: {prob:.4f}"
for label, prob in zip(labels, probs.tolist())
])
prediction = id2label[probs.argmax().item()]
return tokenization_str, logits_str, probs_str, prediction
# Gradio UI
with gr.Blocks(title="Behind the Pipeline") as demo:
gr.Markdown("""
# 🕵️ Behind the Pipeline
Replicating the [Hugging Face NLP Course](https://huggingface.co/learn/nlp-course/chapter2/2) lesson in an interactive Space.
This demonstrates the **three steps** hidden inside `pipeline("sentiment-analysis")`:
1. **Preprocessing** (Tokenizer)
2. **Model Inference** (Logits)
3. **Post-processing** (Softmax → Probabilities)
""")
with gr.Row():
with gr.Column():
input_text = gr.Textbox(
label="Input Text",
value="I've been waiting for a HuggingFace course my whole life."
)
run_btn = gr.Button("Run Pipeline", variant="primary")
with gr.Column():
prediction = gr.Textbox(label="Final Prediction", interactive=False)
with gr.Row():
with gr.Column():
tokenization = gr.Markdown(label="Step 1: Tokenization")
with gr.Column():
logits = gr.Markdown(label="Step 2: Logits")
with gr.Column():
probabilities = gr.Markdown(label="Step 3: Probabilities")
run_btn.click(
fn=behind_the_pipeline,
inputs=input_text,
outputs=[tokenization, logits, probabilities, prediction]
)
# Examples from the lesson
gr.Examples(
examples=[
"I've been waiting for a HuggingFace course my whole life.",
"I hate this so much!",
"This movie was surprisingly good.",
"The weather today is terrible."
],
inputs=input_text
)
if __name__ == "__main__":
demo.launch() |