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import torch
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Mr-Vicky-01/Gemma-2B-Finetuined-pythonCode")
model = AutoModelForCausalLM.from_pretrained("Mr-Vicky-01/Gemma-2B-Finetuined-pythonCode")
def generate_code(text):
prompt_template = f"""
<start_of_turn>user based on given instruction create a solution\n\nhere are the instruction {text}
<end_of_turn>\n<start_of_turn>model
"""
prompt = prompt_template
encodeds = tokenizer(prompt, return_tensors="pt", add_special_tokens=True).input_ids
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)
inputs = encodeds.to(device)
# Increase max_new_tokens if needed
generated_ids = model.generate(inputs, max_new_tokens=500, do_sample=False, pad_token_id=tokenizer.eos_token_id)
ans = ''
for i in tokenizer.decode(generated_ids[0], skip_special_tokens=True).split('<end_of_turn>')[:2]:
ans += i
# Extract only the model's answer
model_answer = ans.split("model")[1].strip()
return model_answer.split("user")[1]
demo = gr.Interface(fn=generate_code, inputs='text',outputs='text',title='Text Summarization')
demo.launch(debug=True,share=True) |