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from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import gradio as gr
import os
import time
model_id = "alibidaran/Gemma2_Virtual_doctor"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
def print_like_dislike(x: gr.LikeData):
print(x.index, x.value, x.liked)
def add_text(history, text):
history = history + [(text,None)]
return history, gr.Textbox(value="", interactive=False)
def add_file(history, file):
global image_file
image_file=file.name
history = history + [((file.name,),None)]
return history
def bot(history):
prompt=history[-1][0]
text=f"<s> ###Human: {prompt} ###Asistant: "
inputs=tokenizer(text,return_tensors='pt').to('cpu')
with torch.no_grad():
outputs=model.generate(**inputs,max_new_tokens=120,do_sample=True,top_p=0.92,top_k=10,temperature=0.7)
output_text=outputs[:, inputs.input_ids.shape[1]:]
response=tokenizer.decode(output_text[0], skip_special_tokens=True)
print(response)
history[-1][1] = ""
for character in response[1:-1]:
history[-1][1] += character
time.sleep(0.01)
yield history
with gr.Blocks() as demo:
chatbot = gr.Chatbot(
[],
elem_id="chatbot",
bubble_full_width=False,
#avatar_images=(None, (os.path.join(os.path.dirname(__file__), "avatar.png"))),
)
with gr.Row():
txt = gr.Textbox(
scale=4,
show_label=False,
placeholder="Ask the virtual doctor about your symptoms!",
container=False,
)
btn = gr.UploadButton("📁", file_types=["image", "video", "audio"])
txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
bot, chatbot, chatbot, api_name="bot_response"
)
txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)
file_msg = btn.upload(add_file, [chatbot, btn], [chatbot], queue=False).then(
bot, chatbot, chatbot
)
chatbot.like(print_like_dislike, None, None)
if __name__=="__main__":
demo.launch(share=True,debug=True) |