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2f38d24 6fa29e0 | 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 | import gradio as gr
import torch
import torch.nn.functional as F
import tiktoken
from model import GPT, GPTConfig # We'll create this file next
from huggingface_hub import hf_hub_download
# Initialize model and load weights
def load_model():
model = GPT(GPTConfig(
block_size=128,
vocab_size=50257,
n_layer=12,
n_head=12,
n_embd=768
))
# Download the model from the Hub
model_path = hf_hub_download(
repo_id="amlanr66/gpt2TransformerCustomeTrained", # Replace with your model repository
filename="best_model.pt"
)
checkpoint = torch.load(model_path, map_location=torch.device('cpu'))
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
return model
# Text generation function
def generate_text(prompt, max_length=50, temperature=0.8, top_k=50):
# Encode the prompt
enc = tiktoken.get_encoding('gpt2')
input_ids = torch.tensor(enc.encode(prompt)).unsqueeze(0) # Add batch dimension
# Generate text
with torch.no_grad():
for _ in range(max_length):
# Get the last block_size tokens
input_ids_cond = input_ids[:, -model.config.block_size:]
# Forward pass
logits = model(input_ids_cond)[0]
logits = logits[:, -1, :] / temperature
# Apply top-k sampling
if top_k is not None:
v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
logits[logits < v[:, [-1]]] = float('-inf')
# Sample from the distribution
probs = F.softmax(logits, dim=-1)
next_token = torch.multinomial(probs, num_samples=1)
# Append the new token
input_ids = torch.cat((input_ids, next_token), dim=1)
# Decode the generated text
generated_text = enc.decode(input_ids[0].tolist())
return generated_text
# Load the model globally
model = load_model()
# Create Gradio interface
def predict(text, length, temp):
try:
output = generate_text(
prompt=text,
max_length=int(length),
temperature=float(temp)
)
return output
except Exception as e:
return f"Error: {str(e)}"
# Define the interface
iface = gr.Interface(
fn=predict,
inputs=[
gr.Textbox(label="Input Text", placeholder="Enter your prompt here..."),
gr.Slider(minimum=10, maximum=200, value=50, step=1, label="Maximum Length"),
gr.Slider(minimum=0.1, maximum=2.0, value=0.8, step=0.1, label="Temperature")
],
outputs=gr.Textbox(label="Generated Text"),
title="GPT Text Generation",
description="Enter a prompt and the model will continue the text.",
examples=[
["The quick brown fox", 50, 0.8],
["Once upon a time", 100, 0.9],
["In a world where", 75, 0.7]
]
)
# Launch the app
if __name__ == "__main__":
iface.launch() |