#conda create -n gradioenv
#conda activate gradioenv
#conda install -c conda-forge gradio
#don't forget to set conda env as interpreter in VS Code 

import requests, json

import gradio as gr


model = 'phi3' #llama3
context = []

import gradio as gr

#Call Ollama API
def generate(prompt, context, top_k, top_p, temp):
    r = requests.post('http://localhost:11434/api/generate',
                     json={
                         'model': model,
                         'prompt': prompt,
                         'context': context,
                         'options':{
                             'top_k': top_k,
                             'temperature':top_p,
                             'top_p': temp
                         }
                     },
                     stream=False)
    r.raise_for_status()

 
    response = ""  

    for line in r.iter_lines():
        body = json.loads(line)
        response_part = body.get('response', '')
        print(response_part)
        if 'error' in body:
            raise Exception(body['error'])

        response += response_part

        if body.get('done', False):
            context = body.get('context', [])
            return response, context
              
def chat(input, chat_history, top_k, top_p, temp):

    chat_history = chat_history or []

    global context
    output, context = generate(input, context, top_k, top_p, temp)

    chat_history.append((input, output))

    return chat_history, chat_history
  #the first history in return history, history is meant to update the 
  #chatbot widget, and the second history is meant to update the state 
  #(which is used to maintain conversation history across interactions)

#########################Gradio Code##########################
block = gr.Blocks()


with block:

    gr.Markdown("""<h1><center> Arztbrief Center </center></h1>
    """)

    chatbot = gr.Chatbot()
    message = gr.Textbox(placeholder="Ihre Nachricht")

    state = gr.State()
    with gr.Row():
        top_k = gr.Slider(0.0,100.0, label="top_k", value=40, info="Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40)")
        top_p = gr.Slider(0.0,1.0, label="top_p", value=0.9, info=" Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)")
        temp = gr.Slider(0.0,2.0, label="temperature", value=0.8, info="The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8)")


    submit = gr.Button("SEND")

    submit.click(chat, inputs=[message, state, top_k, top_p, temp], outputs=[chatbot, state])

#To create a public link, set `share=True` in `launch()`.
block.launch(debug=True, share=True)