app.py

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):
    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()