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| 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() |