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"""
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load your fine-tuned model and tokenizer
model_name = "crystal99/my-fine-tuned-model"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the text generation function
def generate_text(prompt):
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(inputs['input_ids'], max_length=100, num_return_sequences=1)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=False)
return generated_text
# Set up the Gradio interface
iface = gr.Interface(fn=generate_text, inputs="text", outputs="text", title="Text Generator using Fine-Tuned Model")
# Launch the Gradio interface
iface.launch()
"""
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load your fine-tuned model and tokenizer
model_name = "crystal99/my-fine-tuned-model" #crystal99/my-fine-tuned-model
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Move model to GPU if available and enable fp16 for faster inference
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
# Define the text generation function
def generate_text(prompt):
# Prevent gradient calculation to speed up inference
with torch.no_grad():
inputs = tokenizer(f"<|STARTOFTEXT|> <|USER|> {prompt} <|BOT|>", return_tensors="pt").to(device)
outputs = model.generate(inputs['input_ids'], max_length=100, num_return_sequences=1, do_sample=False)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=False)
result2 = generated_text.split("<|ENDOFTEXT|>")
finalRes = result2[0].split("<|BOT|>")
print(generated_text)
return finalRes[-1]
# Set up the Gradio interface
iface = gr.Interface(fn=generate_text, inputs="text", outputs="text", title="Text Generator using Fine-Tuned Model")
# Launch the Gradio interface
iface.launch()
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