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| import gradio as gr | |
| # gr.load("models/shivanikerai/TinyLlama-1.1B-Chat-v1.0-seo-optimised-title-suggestion-v1.0").launch() | |
| # Load model directly | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| tokenizer = AutoTokenizer.from_pretrained("shivanikerai/TinyLlama-1.1B-Chat-v1.0-seo-optimised-title-suggestion-v1.0") | |
| model = AutoModelForCausalLM.from_pretrained("shivanikerai/TinyLlama-1.1B-Chat-v1.0-seo-optimised-title-suggestion-v1.0") | |
| def generate_title_suggestions(keywords, product_info): | |
| # Define the roles and markers | |
| B_SYS, E_SYS = "<<SYS>>", "<</SYS>>" | |
| B_INST, E_INST = "[INST]", "[/INST]" | |
| B_in, E_in = "[Product Details]", "[/Product Details]" | |
| B_out, E_out = "[Suggested Titles]", "[/Suggested Titles]" | |
| # Format your prompt template | |
| prompt = f"""{B_INST} {B_SYS} You are a helpful, respectful and honest assistant for ecommerce product title creation. {E_SYS}\nCreate a SEO optimized e-commerce product title for the keywords:{keywords.strip()}\n{B_in}{product_info}{E_in}\n{E_INST}\n\n{B_out}""" | |
| print("Prompt:") | |
| print(prompt) | |
| encoding = tokenizer(prompt, return_tensors="pt").to("cuda:0") | |
| output = model.generate(input_ids=encoding.input_ids, | |
| attention_mask=encoding.attention_mask, | |
| max_new_tokens=1024, | |
| do_sample=True, | |
| temperature=0.01, | |
| eos_token_id=tokenizer.eos_token_id, | |
| top_k=0) | |
| print() | |
| # Subtract the length of input_ids from output to get only the model's response | |
| output_text = tokenizer.decode(output[0, len(encoding.input_ids[0]):], skip_special_tokens=False) | |
| output_text = re.sub('\n+', '\n', output_text) # remove excessive newline characters | |
| print("Generated Assistant Response:") | |
| print(output_text) | |
| gr.Interface( | |
| generate_title_suggestions, | |
| inputs='text', | |
| outputs='text', | |
| title="Title Suggestion", | |
| ).launch() | |