import streamlit as st

import os
import requests
from gradio_client import Client

API_TOKEN = st.secrets['API_TOKEN']
API_URL = "https://api-inference.huggingface.co/models/mistralai/Mixtral-8x7B-Instruct-v0.1"
headers = {"Authorization": "Bearer {API_TOKEN}"}

def query(payload):
	response = requests.post(API_URL, headers=headers, json=payload)
	return response.json()
	
def translate(text,source="English",target="Moroccan Arabic"):
    client = Client("https://facebook-seamless-m4t-v2-large.hf.space/--replicas/6w5sk/")
    result = client.predict(
            text,	# str  in 'Input text' Textbox component
            source,	# Literal[Afrikaans, Amharic, Armenian, Assamese, Basque, Belarusian, Bengali, Bosnian, Bulgarian, Burmese, Cantonese, Catalan, Cebuano, Central Kurdish, Croatian, Czech, Danish, Dutch, Egyptian Arabic, English, Estonian, Finnish, French, Galician, Ganda, Georgian, German, Greek, Gujarati, Halh Mongolian, Hebrew, Hindi, Hungarian, Icelandic, Igbo, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kyrgyz, Lao, Lithuanian, Luo, Macedonian, Maithili, Malayalam, Maltese, Mandarin Chinese, Marathi, Meitei, Modern Standard Arabic, Moroccan Arabic, Nepali, North Azerbaijani, Northern Uzbek, Norwegian Bokmål, Norwegian Nynorsk, Nyanja, Odia, Polish, Portuguese, Punjabi, Romanian, Russian, Serbian, Shona, Sindhi, Slovak, Slovenian, Somali, Southern Pashto, Spanish, Standard Latvian, Standard Malay, Swahili, Swedish, Tagalog, Tajik, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Vietnamese, Welsh, West Central Oromo, Western Persian, Yoruba, Zulu]  in 'Source language' Dropdown component
            target,	# Literal[Afrikaans, Amharic, Armenian, Assamese, Basque, Belarusian, Bengali, Bosnian, Bulgarian, Burmese, Cantonese, Catalan, Cebuano, Central Kurdish, Croatian, Czech, Danish, Dutch, Egyptian Arabic, English, Estonian, Finnish, French, Galician, Ganda, Georgian, German, Greek, Gujarati, Halh Mongolian, Hebrew, Hindi, Hungarian, Icelandic, Igbo, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kyrgyz, Lao, Lithuanian, Luo, Macedonian, Maithili, Malayalam, Maltese, Mandarin Chinese, Marathi, Meitei, Modern Standard Arabic, Moroccan Arabic, Nepali, North Azerbaijani, Northern Uzbek, Norwegian Bokmål, Norwegian Nynorsk, Nyanja, Odia, Polish, Portuguese, Punjabi, Romanian, Russian, Serbian, Shona, Sindhi, Slovak, Slovenian, Somali, Southern Pashto, Spanish, Standard Latvian, Standard Malay, Swahili, Swedish, Tagalog, Tajik, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Vietnamese, Welsh, West Central Oromo, Western Persian, Yoruba, Zulu]  in 'Target language' Dropdown component
                                api_name="/t2tt"
    )
    print(result)
    return result


# Function to generate a response from the chatbot
def get_bot_response(user_input):
    location = 'Benguerir, Morocco'
    soil_type = 'red soil'
    humidity = '40%'
    weather = 'Sunny'
    temp = '19C'
    agriculture = 'olives'

    # Add your chatbot logic here
    # For simplicity, the bot echoes the user's input in this example

    instruction = f'''
    <s> [INST] You are an agriculture expert, Given the following informations, geographical coordinates (latitude and longitude), prevailing weather conditions, specific land type, chosen type of agriculture, and soil composition of a designated area, request the LLM to provide detailed insights and predictions on optimal agricultural practices, potential crop yields, and recommended soil management strategies, or answer the question below
Location: {location},
land type: {soil_type}
humidity: {humidity}
weather: {weather}
temperature: {temp}
agriculture: {agriculture} [/INST]</s>
    '''
    prompt = f'''
    You are an agriculture expert, Given the following informations, geographical coordinates (latitude and longitude), prevailing weather conditions, specific land type, chosen type of agriculture, and soil composition of a designated area, request the LLM to provide detailed insights and predictions on optimal agricultural practices, potential crop yields, and recommended soil management strategies, or answer the question below
Location: {location},
land type: {soil_type}
humidity: {humidity}
weather: {weather}
temperature: {temp}
agriculture: {agriculture}
    '''
#     output = query({"inputs": f'''
# PROMPT: {prompt}
# QUESTION: {user_input}
# ANSWER: 
# ''',})

    output = query({"inputs": instruction, "parameters":{"max_new_tokens":250, "temperature":0.4, "return_full_text":False}})

    return f"Bot: {output[0]['generated_text']}"

# Streamlit app
def main():
    st.title("Simple Chatbot")

    # Initialize chat history in session state
    if "chat_history" not in st.session_state:
        st.session_state.chat_history = []

    # Text input for user to enter messages
    user_input = st.text_input("You:", "")

    # Button to send the message and get the bot's response
    if st.button("Send"):
        # Add user input to the chat history
        st.session_state.chat_history.append(f"You: {user_input}")

        # Get bot response and add it to the chat history
        bot_response = get_bot_response(user_input)
        st.session_state.chat_history.append(bot_response)

    # Display the chat history using a text area
    st.text_area("Chat History:", "\n".join(st.session_state.chat_history), height=200)

    if st.button("Clear Chat"):
        st.session_state.chat_history = []

# Run the Streamlit app
if __name__ == "__main__":
    main()