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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model = AutoModelForCausalLM.from_pretrained(
"Cogwisechat/falcon-7b-finance",
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
low_cpu_mem_usage=True,
)
tokenizer = AutoTokenizer.from_pretrained("Cogwisechat/falcon-7b-finance")
def generate_text(input_text):
global output_text
input_ids = tokenizer.encode(input_text, return_tensors="pt")
attention_mask = torch.ones(input_ids.shape)
output = model.generate(
input_ids,
attention_mask=attention_mask,
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
output_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(output_text)
# Remove Prompt Echo from Generated Text
cleaned_output_text = output_text.replace(input_text, "")
return cleaned_output_text
block = gr.Blocks()
with block:
gr.Markdown("""<h1><center>CogwiseAI falcon7b</center></h1>
""")
# chatbot = gr.Chatbot()
message = gr.Textbox(placeholder='Enter Your Question Here')
state = gr.State()
submit = gr.Button("SEND")
submit.click(generate_text, inputs=[message, state], outputs=[output_text, state])
block.launch(debug = True)
# logo = (
# "<div >"
# "<img src='ai-icon.png'alt='image One'>"
# + "</div>"
# )
# text_generation_interface = gr.Interface(
# fn=generate_text,
# inputs=[
# gr.inputs.Textbox(label="Input Text"),
# ],
# outputs=gr.inputs.Textbox(label="Generated Text"),
# title="Falcon-7B Instruct",
# image=logo
# ).launch()
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