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| import gradio as gr | |
| from transformers import pipeline | |
| # Define all pipelines | |
| def load_pipelines(): | |
| pipelines = { | |
| "GPT-2 Original": pipeline("text-generation", model="gpt2"), | |
| "GPT-2 Medium": pipeline("text-generation", model="gpt2-medium"), | |
| "DistilGPT-2": pipeline("text-generation", model="distilgpt2"), | |
| "German GPT-2": pipeline("text-generation", model="german-nlp-group/german-gpt2"), | |
| "German Wechsel GPT-2": pipeline("text-generation", model="benjamin/gpt2-wechsel-german"), | |
| "T5 Base": pipeline("text-generation", model="t5-base"), | |
| "T5 Large": pipeline("text-generation", model="t5-large"), | |
| "Text Classification": pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english"), | |
| "Sentiment Analysis": pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment") | |
| } | |
| return pipelines | |
| def respond( | |
| message, | |
| history: list[tuple[str, str]], | |
| system_message, | |
| model_name, | |
| max_tokens, | |
| temperature, | |
| top_p, | |
| ): | |
| # Load pipelines | |
| pipelines = load_pipelines() | |
| pipe = pipelines.get(model_name) | |
| if not pipe: | |
| return "Error: Model not found." | |
| # For text generation models | |
| if model_name in ["GPT-2 Original", "GPT-2 Medium", "DistilGPT-2", | |
| "German GPT-2", "German Wechsel GPT-2", | |
| "T5 Base", "T5 Large"]: | |
| # Prepare full prompt | |
| full_history = ' '.join([f"{msg[0]} {msg[1] or ''}" for msg in history]) if history else '' | |
| full_prompt = f"{system_message}\n{full_history}\nUser: {message}\nAssistant:" | |
| try: | |
| response = pipe( | |
| full_prompt, | |
| max_length=len(full_prompt) + max_tokens, | |
| temperature=temperature, | |
| top_p=top_p, | |
| num_return_sequences=1 | |
| )[0]['generated_text'] | |
| # Extract just the new assistant response | |
| assistant_response = response[len(full_prompt):].strip() | |
| return assistant_response | |
| except Exception as e: | |
| return f"Generation error: {e}" | |
| # For classification and sentiment models | |
| elif model_name == "Text Classification": | |
| try: | |
| result = pipe(message)[0] | |
| return f"Classification: {result['label']} (Confidence: {result['score']:.2f})" | |
| except Exception as e: | |
| return f"Classification error: {e}" | |
| elif model_name == "Sentiment Analysis": | |
| try: | |
| result = pipe(message)[0] | |
| return f"Sentiment: {result['label']} (Confidence: {result['score']:.2f})" | |
| except Exception as e: | |
| return f"Sentiment analysis error: {e}" | |
| def create_chat_interface(): | |
| """Create Gradio ChatInterface with model selection.""" | |
| demo = gr.ChatInterface( | |
| respond, | |
| additional_inputs=[ | |
| gr.Textbox(value="You are a helpful assistant.", label="System message"), | |
| gr.Dropdown( | |
| ["GPT-2 Original", "GPT-2 Medium", "DistilGPT-2", | |
| "German GPT-2", "German Wechsel GPT-2", | |
| "T5 Base", "T5 Large", | |
| "Text Classification", "Sentiment Analysis"], | |
| value="GPT-2 Original", | |
| label="Select Model" | |
| ), | |
| gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), | |
| gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), | |
| gr.Slider( | |
| minimum=0.1, | |
| maximum=1.0, | |
| value=0.95, | |
| step=0.05, | |
| label="Top-p (nucleus sampling)", | |
| ), | |
| ] | |
| ) | |
| return demo | |
| if __name__ == "__main__": | |
| chat_interface = create_chat_interface() | |
| chat_interface.launch(share=True) |