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import gradio as gr
import shutil
import os
import ollama
import time
#import pandas as pd
global Modelfile # Declare Modelfile as a global variable
#def Generate_report(history,model_flags):
# data={'steps':model_flags,
# 'turns':history}
# dataframe=pd.DataFrame.from_dict(data)
#dataframe.to_csv('Repports.csv',index=False)
def user(user_message,history):
return "", history+[{'role': 'user', 'content':user_message}]
def respond(history):
text=f"<s> ###Human: {history[-1]['content']} ###Asistant: "
response=ollama.generate(
model='LLAMA3.2 Virtual_doctor2:latest',
prompt=text,
stream=False,
)
history.append({'role':'assistant','content':""})
for character in response['response']:
history[-1]['content']+=character
time.sleep(0.02)
yield history
with gr.Blocks() as demo:
gr.Markdown('# AI Therapist for (CBT and FIT)')
with gr.Tab('Chat Interface'):
gr.HTML('<h1> Fake client chatbot </h2>')
chatbot = gr.Chatbot(type="messages")
msg = gr.Textbox()
btn=gr.Button('Send')
clear = gr.ClearButton([msg, chatbot])
btn.click(user, [msg, chatbot], [msg, chatbot],queue=False).then(respond,chatbot,chatbot)
clear.click(lambda:None,None,chatbot,queue=False)
if __name__=='__main__':
demo.launch(server_name="0.0.0.0", server_port=7860)
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