import base64 import streamlit as st from streamlit_chat import message from streamlit_extras.colored_header import colored_header from streamlit_extras.add_vertical_space import add_vertical_space import requests from gradio_client import Client import datetime as dt import urllib.parse st.set_page_config(page_title="HugChat - An LLM-powered Streamlit app") API_TOKEN = st.secrets['HF_TOKEN'] API_URL = "https://api-inference.huggingface.co/models/mistralai/Mixtral-8x7B-Instruct-v0.1" headers = {"Authorization": f"Bearer {str(API_TOKEN)}"} def get_text(): input_text = st.text_input("You: ", "", key="input") return input_text soil_types = { "Sais plain": "Brown limestone, vertisols, lithosols, and regosols", "Chaouïa, Doukkala, and Abda plains": "Rendzines associated with lithosols in the Atlantic coast and isohumic and vertisols inland", "Eastern High Plateaux and Moulouya Valley": "Sierozems and fluvisols", "Rif": "Brown soils associated with lithosols and regosols or vertisols", "Mamora and Zemmour plateau": "Sandy soil", "Middle Atlas": "Brown soils and rendzinas", "High Atlas": "Lithosols and regosols, in association with brown soils and sierozems", "Loukkos": "Mostly gleysols and brunified", "Rharb plain": "Gleysols and vertisols", "Central plateau": "In forested areas, soils are brown associated with lithosols and regosols. Elsewhere (Zaer), vertisols and gleysols dominate", "Plains and plateaux of north of the Atlas": "Lithosols (Rehamnas, Jebilete), sierozems associated with lithosols", "Argan zone": "Soils are mostly lithosols and regosols, associated with fluvisols and saline soils on lowlands", "Presaharan soils": "Lithosols and regosols in association with sierozems and regs", "Saharan zone": "Yermosols, associated with sierozems, lithosols, and saline soils" } def get_weather_data(city): base_url = "http://api.openweathermap.org/data/2.5/weather?" api_key = "84f9ed7c3738f567e5f1cbf2068d96a6" # API key encoded_api_key = urllib.parse.quote(api_key) # URL encoding the API key url = base_url + "appid=" + encoded_api_key + "&q=" + city try: response = requests.get(url) response.raise_for_status() weather_data = response.json() # Processing the weather data temp_kelvin = weather_data['main']['temp'] humidity = weather_data['main']['humidity'] weather_condition = weather_data['weather'][0]['description'] # Converting temperature to Celsius temp_celsius = temp_kelvin - 273.15 return { "temperature": f"{temp_celsius:.2f}C", "humidity": f"{humidity}%", "weather_condition": weather_condition } except requests.exceptions.HTTPError as err: print(f"HTTP error: {err}") return None except requests.exceptions.RequestException as e: print(f"Error: {e}") return None # Example usage 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/2bmbx/") 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 generate_response(user_input,region): city = "Fez" weather_info = get_weather_data(city) if weather_info: print(weather_info) user_input_translated = str(translate(user_input, "Moroccan Arabic", "English")) name = 'Fellah(a)' date = 'December' location = 'Fes, Morocco' soil_type = soil_types[region] # Use the selected region's soil type humidity = weather_info["humidity"] weather = weather_info["weather_condition"] temp = weather_info["temperature"] # agriculture = 'olives' # Add your chatbot logic here # For simplicity, the bot echoes the user's input in this example instruction = f''' [INST] You are an agriculture expert, and my name is {name} Given the following informations, prevailing weather conditions, specific land type, chosen type of agriculture, and soil composition of a designated area, answer the question below Location: {location}, Current Month : {date} land type: {soil_types[region]} humidity: {humidity} weather: {weather} temperature: {temp} Question: {user_input_translated}[/INST] ''' 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} ''' # output = query({"inputs": f''' # PROMPT: {prompt} # QUESTION: {user_input} # ANSWER: # ''',}) output = query({"inputs": instruction, "parameters":{"max_new_tokens":250, "temperature":1, "return_full_text":False}}) # print(headers) print(instruction) print(output) return f"Bot: {translate(output[0]['generated_text'])}" def sidebar_bg(side_bg): side_bg_ext = 'png' st.markdown( f""" """, unsafe_allow_html=True, ) def main(): # Sidebar contents with st.sidebar: st.title('Smart فْلاّح 🌱👩🏻‍🌾') st.markdown(''' ## About Smart فلاح , an innovative AI-based platform developed in Morocco, uses machine learning, image processing, and harnesses the power of Large Language Models to offer real-time crop insights to farmers in a customized and friendly way. This solution is tailored to the unique agricultural landscape and challenges of Morocco or Africa. 💡 Note: No API key required! ''') add_vertical_space(5) st.write('Made with ❤️ by [llama-crew](https://huggingface.co/llama-crew)') #st.write('Made with ❤️ by [llama-crew](https://hf.co/medmac01)') # Generate empty lists for generated and past. ## generated stores AI generated responses if 'generated' not in st.session_state: st.session_state['generated'] = ["واحد السلام عليكم 👋🏻، كيفاش نقدر نعاونك؟"] ## past stores User's questions if 'past' not in st.session_state: st.session_state['past'] = ['سلام!'] # sidebar_bg('bg.jpg') # Layout of input/response containers input_container = st.container() selected_region = st.selectbox("Choose a region:", list(soil_types.keys())) if st.button("Clear Chat"): st.session_state['past'] = [] st.session_state['generated'] = [] colored_header(label='', description='', color_name='blue-30') response_container = st.container() # User input ## Function for taking user provided prompt as input ## Applying the user input box with input_container: user_input = get_text() # Response output ## Function for taking user prompt as input followed by producing AI generated responses ## Conditional display of AI generated responses as a function of user provided prompts with response_container: if user_input: response = generate_response(user_input,str(selected_region)) st.session_state.past.append(user_input) st.session_state.generated.append(response) if st.session_state['generated']: for i in range(len(st.session_state['generated'])): message(st.session_state['past'][i], is_user=True, key=str(i) + '_user', logo="https://i.pinimg.com/originals/d5/b2/13/d5b21384ccaaa6f9ef32986f17c50638.png") message(st.session_state["generated"][i], key=str(i), logo= "https://emojiisland.com/cdn/shop/products/Robot_Emoji_Icon_7070a254-26f7-4a54-8131-560e38e34c2e_large.png?v=1571606114") if __name__ == "__main__": main()