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6141866 fc6aa6c 6141866 77c1734 6141866 77c1734 6141866 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 | import streamlit as st
import torch
import tiktoken
from model import GPT, GPTConfig
from transformers import GPT2LMHeadModel
@st.cache_resource
def get_model():
"""Load the trained GPT model."""
model = GPT(GPTConfig())
# Load from the Hugging Face Hub instead of local file
model_path = 'mathminakshi/custom_gpt2'
model.load_state_dict(torch.hub.load_state_dict_from_url(f'https://huggingface.co/{model_path}/resolve/main/best_model.pth', map_location='cpu')['model_state_dict'])
model.eval()
return model
def generate_text(prompt, max_tokens=500, temperature=0.3, top_k=40):
"""Generate text based on the prompt."""
# Encode the prompt
enc = tiktoken.get_encoding('gpt2')
input_ids = torch.tensor(enc.encode(prompt)).unsqueeze(0)
# Get cached model
model = get_model()
with torch.no_grad():
output_sequence = []
progress_bar = st.progress(0)
for i in range(max_tokens):
progress_bar.progress(i / max_tokens)
# Get predictions
outputs = model(input_ids)
logits = outputs.logits[:, -1, :] / temperature
# Apply top-k filtering
if top_k > 0:
indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
logits[indices_to_remove] = float('-inf')
# Sample from the filtered distribution
probs = torch.nn.functional.softmax(logits, dim=-1)
next_token = torch.multinomial(probs, num_samples=1)
# Append to output
output_sequence.append(next_token.item())
input_ids = torch.cat([input_ids, next_token], dim=1)
# Stop if we generate an EOS token
if next_token.item() == 50256:
break
progress_bar.progress(1.0)
generated_text = enc.decode(output_sequence)
return prompt + generated_text
def main():
st.title("GPT Text Generator")
st.write("Enter a prompt to generate text using GPT-2.")
# Sidebar for parameters
st.sidebar.header("Generation Parameters")
max_tokens = st.sidebar.slider(
"Max Tokens",
min_value=1,
max_value=1000,
value=100,
help="Maximum number of tokens to generate"
)
temperature = st.sidebar.slider(
"Temperature",
min_value=0.1,
max_value=2.0,
value=0.8,
help="Higher values make the output more random"
)
top_k = st.sidebar.slider(
"Top-K",
min_value=1,
max_value=100,
value=40,
help="Limits the number of tokens to choose from"
)
prompt = st.text_area(
"Enter your prompt:",
height=100,
placeholder="Once upon a time..."
)
if st.button("Generate"):
if prompt:
with st.spinner("Generating text..."):
generated_text = generate_text(
prompt=prompt,
max_tokens=max_tokens,
temperature=temperature,
top_k=top_k
)
st.write("### Generated Text:")
st.write(generated_text)
else:
st.warning("Please enter a prompt first!")
if __name__ == "__main__":
main() |