| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| checkpoint_name="ArmelR/starcoder-gradio-v0" | |
| model = AutoModelForCausalLM.from_pretrained(checkpoint_name) | |
| tokenizer = AutoTokenizer.from_pretrained(checkpoint_name) | |
| def generate_text(inp): | |
| prompt = "Create a gradio application that help to convert temperature in celcius into temperature in Fahrenheit" | |
| inputs = tokenizer(f"Question: {prompt}\n\nAnswer: ", return_tensors="pt") | |
| outputs = model.generate( | |
| inputs["input_ids"], | |
| temperature=0.2, | |
| top_p=0.95, | |
| max_new_tokens=200 | |
| ) | |
| input_len=len(inputs["input_ids"]) | |
| print(tokenizer.decode(outputs[0][input_len:])) | |
| output_text = gr.outputs.Textbox() | |
| gr.Interface(generate_text,"textbox",output_text,title="Text Generation machine ",description="Ask any question. Note: It can take 20-60 seconds to generate output based on your internet connection.").launch() |