import streamlit as st
import requests
import json



st.set_page_config(page_title="Generate Therapy Answers🤖",
            page_icon='🤖',
            layout='centered',
            initial_sidebar_state='collapsed')


## Function To get response from LLAma 2 model

# def getLLamaresponse(input_text):

#     ### LLama2 model
#     llm=CTransformers(model='models/openorca_7b_chat_uncensored_FT_GGUF.gguf',
#               model_type='llama',
#               config={'max_new_tokens':256,
#                   'temperature':0.01,
#                   'gpu_layers':25,
#                   })
#     ## Prompt Template


url = "https://localhost/api/generate"

headers = {
    'Content-Type': 'application/json',
}


def generate_text():
    # prompt = f"Please generate a draft for a legal notice in detail. The notice is to be sent on behalf of {client_name}, located at {client_address}, to {recipient_name} regarding {reason_for_notice}. The notice should include a clear statement of the issue, a request for resolution or action, a deadline for response or action, and any legal consequences of non-compliance. Please use formal language and ensure the notice is legally sound.\n\nCrime Type: include any IPC that applies to this perticular case"
    prompt = f"PRovide Response on the below text \n\n{input_text}"


    data = {
        "model": "openorca_FT_medical",
        "stream": False,
        "prompt": prompt,
    }
    response = requests.post(url, headers=headers, data=json.dumps(data))

    if response.status_code == 200:
        response_text = response.text
        data = json.loads(response_text)
        actual_response = data["response"]
        return actual_response
    else:
        st.error(f"Error: {response.status_code}, {response.text}")


st.header("Therapy Provider 🤖")
input_text=st.text_area("Enter your Problem/Emotions")

if st.button("Generate Response"):
    generated_notice = generate_text()
    st.text_area("Generated Legal Notice", generated_notice)