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| import spaces | |
| import torch | |
| import gradio as gr | |
| from transformers import ( | |
| AutoTokenizer, | |
| AutoModelForSequenceClassification, | |
| AutoModelForMaskedLM, | |
| AutoModelForSeq2SeqLM, | |
| AutoModelForCausalLM, # إضافة لدعم GPT | |
| ) | |
| # ============================================================ | |
| # Device | |
| # ============================================================ | |
| DEVICE = "cuda" if torch.cuda.is_available() else "cpu" | |
| print("=" * 60) | |
| print("Transformer Playground") | |
| print(f"Device: {DEVICE}") | |
| print("=" * 60) | |
| # ============================================================ | |
| # Models | |
| # ============================================================ | |
| print("\nLoading DistilBERT...") | |
| sentiment_tokenizer = AutoTokenizer.from_pretrained( | |
| "distilbert/distilbert-base-uncased-finetuned-sst-2-english" | |
| ) | |
| sentiment_model = AutoModelForSequenceClassification.from_pretrained( | |
| "distilbert/distilbert-base-uncased-finetuned-sst-2-english" | |
| ) | |
| sentiment_model.to(DEVICE) | |
| sentiment_model.eval() | |
| print("\nLoading BERT...") | |
| mlm_tokenizer = AutoTokenizer.from_pretrained( | |
| "google-bert/bert-base-uncased" | |
| ) | |
| mlm_model = AutoModelForMaskedLM.from_pretrained( | |
| "google-bert/bert-base-uncased" | |
| ) | |
| mlm_model.to(DEVICE) | |
| mlm_model.eval() | |
| print("\nLoading DistilBART...") | |
| summarization_tokenizer = AutoTokenizer.from_pretrained( | |
| "sshleifer/distilbart-cnn-12-6" | |
| ) | |
| summarization_model = AutoModelForSeq2SeqLM.from_pretrained( | |
| "sshleifer/distilbart-cnn-12-6" | |
| ) | |
| summarization_model.to(DEVICE) | |
| summarization_model.eval() | |
| print("\nLoading GPT-2...") # تحميل نموذج GPT-2 | |
| gpt_tokenizer = AutoTokenizer.from_pretrained("gpt2") | |
| gpt_model = AutoModelForCausalLM.from_pretrained("gpt2") | |
| gpt_model.to(DEVICE) | |
| gpt_model.eval() | |
| # إضافة رمز الحشو لتجنب التحذيرات | |
| gpt_tokenizer.pad_token = gpt_tokenizer.eos_token | |
| print("\nAll models loaded successfully.") | |
| print("=" * 60) | |
| # ============================================================ | |
| # 1. DistilBERT - Sentiment Classification | |
| # ============================================================ | |
| def analyze_sentiment(text): | |
| if not text or not text.strip(): | |
| return "Please enter some text." | |
| inputs = sentiment_tokenizer( | |
| text, | |
| return_tensors="pt", | |
| truncation=True, | |
| max_length=512 | |
| ) | |
| inputs = { | |
| key: value.to(DEVICE) | |
| for key, value in inputs.items() | |
| } | |
| with torch.no_grad(): | |
| outputs = sentiment_model(**inputs) | |
| probabilities = torch.softmax(outputs.logits, dim=-1)[0] | |
| negative_score = probabilities[0].item() | |
| positive_score = probabilities[1].item() | |
| label = "POSITIVE" if positive_score > negative_score else "NEGATIVE" | |
| score = max(positive_score, negative_score) | |
| return ( | |
| f"Prediction: {label}\n" | |
| f"Confidence: {score:.2%}\n\n" | |
| f"Positive: {positive_score:.2%}\n" | |
| f"Negative: {negative_score:.2%}" | |
| ) | |
| # ============================================================ | |
| # 2. BERT - Masked Language Modeling | |
| # ============================================================ | |
| def predict_mask(text): | |
| if "[MASK]" not in text: | |
| return "Please include [MASK] in your sentence." | |
| inputs = mlm_tokenizer( | |
| text, | |
| return_tensors="pt" | |
| ) | |
| inputs = { | |
| key: value.to(DEVICE) | |
| for key, value in inputs.items() | |
| } | |
| mask_token_index = torch.where( | |
| inputs["input_ids"] == mlm_tokenizer.mask_token_id | |
| )[1] | |
| if len(mask_token_index) == 0: | |
| return "No [MASK] token detected." | |
| with torch.no_grad(): | |
| outputs = mlm_model(**inputs) | |
| logits = outputs.logits | |
| mask_logits = logits[0, mask_token_index[0]] | |
| top_tokens = torch.topk( | |
| mask_logits, | |
| k=5 | |
| ).indices | |
| results = [] | |
| for token_id in top_tokens: | |
| token = mlm_tokenizer.decode([token_id]) | |
| results.append(token.strip()) | |
| return "\n".join( | |
| [ | |
| "Top predictions:", | |
| "", | |
| *[ | |
| f"{i + 1}. {token}" | |
| for i, token in enumerate(results) | |
| ] | |
| ] | |
| ) | |
| # ============================================================ | |
| # 3. DistilBART - Summarization | |
| # ============================================================ | |
| def summarize_text(text): | |
| if not text or not text.strip(): | |
| return "Please enter some text." | |
| inputs = summarization_tokenizer( | |
| text, | |
| return_tensors="pt", | |
| truncation=True, | |
| max_length=1024 | |
| ) | |
| inputs = { | |
| key: value.to(DEVICE) | |
| for key, value in inputs.items() | |
| } | |
| with torch.no_grad(): | |
| summary_ids = summarization_model.generate( | |
| **inputs, | |
| max_length=120, | |
| min_length=30, | |
| num_beams=4, | |
| length_penalty=2.0, | |
| early_stopping=True | |
| ) | |
| summary = summarization_tokenizer.decode( | |
| summary_ids[0], | |
| skip_special_tokens=True | |
| ) | |
| return summary | |
| # ============================================================ | |
| # 4. GPT-2 - Text Generation (Decoder‑only) | |
| # ============================================================ | |
| def generate_text( | |
| prompt: str, | |
| max_length: int = 100, | |
| temperature: float = 0.7, | |
| top_p: float = 0.9 | |
| ): | |
| """ | |
| توليد نصوص باستخدام GPT-2. | |
| """ | |
| if not prompt or not prompt.strip(): | |
| return "Please enter a prompt." | |
| inputs = gpt_tokenizer( | |
| prompt, | |
| return_tensors="pt", | |
| truncation=True, | |
| max_length=512 | |
| ) | |
| inputs = {key: value.to(DEVICE) for key, value in inputs.items()} | |
| with torch.no_grad(): | |
| output_ids = gpt_model.generate( | |
| **inputs, | |
| max_new_tokens=max_length, | |
| do_sample=True, | |
| temperature=temperature, | |
| top_p=top_p, | |
| pad_token_id=gpt_tokenizer.eos_token_id, | |
| repetition_penalty=1.2, | |
| no_repeat_ngram_size=3, | |
| ) | |
| generated_text = gpt_tokenizer.decode( | |
| output_ids[0], | |
| skip_special_tokens=True | |
| ) | |
| return generated_text | |
| # ============================================================ | |
| # Interface | |
| # ============================================================ | |
| with gr.Blocks( | |
| title="Transformer Playground" | |
| ) as demo: | |
| gr.Markdown( | |
| """ | |
| # 🧠 Transformer Playground | |
| ### Transformer ≠ GPT | |
| Explore four different Transformer architectures: | |
| **DistilBERT → Encoder-only → Classification** | |
| **BERT → Encoder-only → Masked Language Modeling** | |
| **DistilBART → Encoder-Decoder → Summarization** | |
| **GPT-2 → Decoder-only → Text Generation** | |
| """ | |
| ) | |
| # -------------------------------------------------------- | |
| # DistilBERT | |
| # -------------------------------------------------------- | |
| with gr.Tab("1️⃣ DistilBERT — Sentiment"): | |
| gr.Markdown( | |
| """ | |
| ## Encoder-only Transformer | |
| DistilBERT reads the input and produces | |
| representations useful for classification. | |
| **Task:** Sentiment Classification | |
| """ | |
| ) | |
| sentiment_input = gr.Textbox( | |
| label="Enter text", | |
| placeholder="I really enjoyed this movie!", | |
| lines=4 | |
| ) | |
| sentiment_button = gr.Button( | |
| "Analyze Sentiment" | |
| ) | |
| sentiment_output = gr.Textbox( | |
| label="Result", | |
| lines=6 | |
| ) | |
| sentiment_button.click( | |
| fn=analyze_sentiment, | |
| inputs=sentiment_input, | |
| outputs=sentiment_output | |
| ) | |
| # -------------------------------------------------------- | |
| # BERT | |
| # -------------------------------------------------------- | |
| with gr.Tab("2️⃣ BERT — Mask Prediction"): | |
| gr.Markdown( | |
| """ | |
| ## Encoder-only Transformer | |
| BERT can understand the surrounding context | |
| of a word by looking at both directions. | |
| **Task:** Masked Language Modeling | |
| Write `[MASK]` where you want BERT to predict a word. | |
| """ | |
| ) | |
| mask_input = gr.Textbox( | |
| label="Sentence", | |
| placeholder="The capital of France is [MASK].", | |
| lines=3 | |
| ) | |
| mask_button = gr.Button( | |
| "Predict Mask" | |
| ) | |
| mask_output = gr.Textbox( | |
| label="Top Predictions", | |
| lines=8 | |
| ) | |
| mask_button.click( | |
| fn=predict_mask, | |
| inputs=mask_input, | |
| outputs=mask_output | |
| ) | |
| # -------------------------------------------------------- | |
| # DistilBART | |
| # -------------------------------------------------------- | |
| with gr.Tab("3️⃣ DistilBART — Summarization"): | |
| gr.Markdown( | |
| """ | |
| ## Encoder-Decoder Transformer | |
| The encoder understands the input, | |
| while the decoder generates the output. | |
| **Task:** Text Summarization | |
| """ | |
| ) | |
| summary_input = gr.Textbox( | |
| label="Long Text", | |
| placeholder="Paste a long article or paragraph here...", | |
| lines=10 | |
| ) | |
| summary_button = gr.Button( | |
| "Summarize" | |
| ) | |
| summary_output = gr.Textbox( | |
| label="Summary", | |
| lines=8 | |
| ) | |
| summary_button.click( | |
| fn=summarize_text, | |
| inputs=summary_input, | |
| outputs=summary_output | |
| ) | |
| # -------------------------------------------------------- | |
| # GPT-2 (جديد) | |
| # -------------------------------------------------------- | |
| with gr.Tab("4️⃣ GPT — Text Generation"): | |
| gr.Markdown( | |
| """ | |
| ## Decoder-only Transformer | |
| GPT generates text autoregressively, predicting | |
| the next token given the previous ones. | |
| **Task:** Open‑ended Text Generation | |
| """ | |
| ) | |
| with gr.Row(): | |
| gpt_prompt = gr.Textbox( | |
| label="Prompt", | |
| placeholder="Once upon a time,", | |
| lines=3, | |
| scale=3 | |
| ) | |
| with gr.Row(): | |
| gpt_max_len = gr.Slider( | |
| label="Max new tokens", | |
| minimum=10, | |
| maximum=200, | |
| value=80, | |
| step=5 | |
| ) | |
| gpt_temp = gr.Slider( | |
| label="Temperature", | |
| minimum=0.1, | |
| maximum=2.0, | |
| value=0.7, | |
| step=0.1 | |
| ) | |
| gpt_top_p = gr.Slider( | |
| label="Top‑p (nucleus sampling)", | |
| minimum=0.5, | |
| maximum=1.0, | |
| value=0.9, | |
| step=0.05 | |
| ) | |
| gpt_button = gr.Button("Generate") | |
| gpt_output = gr.Textbox( | |
| label="Generated Text", | |
| lines=10 | |
| ) | |
| gpt_button.click( | |
| fn=generate_text, | |
| inputs=[gpt_prompt, gpt_max_len, gpt_temp, gpt_top_p], | |
| outputs=gpt_output | |
| ) | |
| # -------------------------------------------------------- | |
| # Architecture Map | |
| # -------------------------------------------------------- | |
| gr.Markdown( | |
| """ | |
| --- | |
| ## 🏗️ Transformer Architecture Map | |
| | Architecture | Model | Task | | |
| |---|---|---| | |
| | Encoder-only | DistilBERT | Classification | | |
| | Encoder-only | BERT | Masked Language Modeling | | |
| | Encoder-Decoder | DistilBART | Summarization | | |
| | Decoder-only | **GPT-2** | **Text Generation** | | |
| ### Main idea | |
| > **Transformer is a family of architectures, not a single model.** | |
| The architecture you choose depends on the task. | |
| """ | |
| ) | |
| # ============================================================ | |
| # Launch | |
| # ============================================================ | |
| if __name__ == "__main__": | |
| demo.launch() |