| import streamlit as st | |
| from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline | |
| def getit(prompt): | |
| generated = tokenizer(f'<|startoftext|> {prompt}', return_tensors="pt").input_ids.cpu() | |
| sample_outputs = sample_outputs = model.generate( | |
| generated, | |
| do_sample=True, | |
| max_length=512, | |
| top_k=50, | |
| top_p=0.95, | |
| num_return_sequences=1, | |
| no_repeat_ngram_size = 3, | |
| temperature = 0.7 | |
| ) | |
| predicted_text = tokenizer.decode(sample_outputs[0], skip_special_tokens=True) | |
| return predicted_text[len(prompt):] | |
| model_name = 'tsaditya/GPT-Kalki' | |
| model = AutoModelWithLMHead.from_pretrained(model_name) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| inp = st.text_input(value="நந்தினி பெரிய பழுவேட்டரையரைப் பார்த்து ",label = "Enter prompt") | |
| if st.button("Generate!"): | |
| out = getit(inp) | |
| st.write(out) | |
| video_file = open(r'myvideo.mp4', 'rb') | |
| video_bytes = video_file.read() | |
| st.video(video_bytes) | |