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| # New code in which sensor data is taken directly from thinkspace. | |
| import os | |
| import json | |
| import requests | |
| from datetime import datetime | |
| import streamlit as st | |
| from langchain_huggingface import HuggingFaceEmbeddings | |
| from langchain_chroma import Chroma | |
| from langchain_groq import ChatGroq | |
| from langchain.memory import ConversationBufferMemory | |
| from langchain.chains import ConversationalRetrievalChain | |
| from deep_translator import GoogleTranslator | |
| # Directory paths and configurations | |
| working_dir = os.path.dirname(os.path.abspath(__file__)) | |
| config_data = json.load(open(f"{working_dir}/config.json")) | |
| GROQ_API_KEY = config_data["GROQ_API_KEY"] | |
| os.environ["GROQ_API_KEY"] = GROQ_API_KEY | |
| # ThinkSpace API details | |
| THINGSPEAK_API_URL = "https://api.thingspeak.com/channels/2485113/feeds.json?results=2" | |
| # Vectorstore setup | |
| def setup_vectorstore(): | |
| embeddings = HuggingFaceEmbeddings() | |
| vectorstore = Chroma(persist_directory="soil_vectordb", embedding_function=embeddings) | |
| return vectorstore | |
| # Chatbot chain setup | |
| def chat_chain(vectorstore): | |
| llm = ChatGroq(model="llama-3.1-70b-versatile", temperature=0) | |
| retriever = vectorstore.as_retriever() | |
| memory = ConversationBufferMemory( | |
| llm=llm, | |
| output_key="answer", | |
| memory_key="chat_history", | |
| return_messages=True | |
| ) | |
| chain = ConversationalRetrievalChain.from_llm( | |
| llm=llm, | |
| retriever=retriever, | |
| chain_type="stuff", | |
| memory=memory, | |
| verbose=True, | |
| return_source_documents=True | |
| ) | |
| return chain | |
| # Fetch sensor data from ThinkSpace API | |
| def fetch_sensor_data(): | |
| try: | |
| response = requests.get(THINGSPEAK_API_URL) | |
| if response.status_code == 200: | |
| data = response.json() | |
| feeds = data.get("feeds", []) | |
| if feeds: | |
| latest_feed = feeds[-1] # Get the latest feed | |
| return { | |
| "pH": float(latest_feed.get("field1", 0)), | |
| "moisture": float(latest_feed.get("field2", 0)), | |
| "temperature": float(latest_feed.get("field3", 0)), | |
| "air_quality": float(latest_feed.get("field4", 0)), | |
| } | |
| else: | |
| st.error("Failed to fetch data from ThinkSpace API. Please check the API URL or connectivity.") | |
| return None | |
| except Exception as e: | |
| st.error(f"An error occurred while fetching sensor data: {e}") | |
| return None | |
| # Updated Streamlit setup with language selection dropdown | |
| st.set_page_config(page_title="Soil.Ai", page_icon="🌱", layout="centered") | |
| st.title("🌱 Soil.Ai - Smart Farming Recommendations") | |
| st.subheader("AI-driven solutions for modern farming!") | |
| # Initialize session state | |
| if "username" not in st.session_state: | |
| username = st.text_input("Enter your name to proceed:") | |
| if username: | |
| with st.spinner("Loading AI interface..."): | |
| st.session_state.username = username | |
| st.session_state.vectorstore = setup_vectorstore() | |
| st.session_state.conversational_chain = chat_chain(st.session_state.vectorstore) | |
| st.session_state.selected_language = "English" # Default language | |
| st.success(f"Welcome, {username}! Start by choosing an option.") | |
| else: | |
| username = st.session_state.username | |
| # Language options | |
| languages = [ | |
| "English", "Marathi", "Hindi", "Bengali", "Gujarati", "Kannada", "Malayalam", | |
| "Odia", "Punjabi", "Tamil", "Telugu", "Urdu", "Spanish", "French", "German" | |
| ] | |
| # Main interface | |
| if "conversational_chain" not in st.session_state: | |
| st.session_state.vectorstore = setup_vectorstore() | |
| st.session_state.conversational_chain = chat_chain(st.session_state.vectorstore) | |
| if "username" in st.session_state: | |
| st.subheader(f"Hello {username}, choose your option below:") | |
| # Dropdown for selecting output language | |
| st.session_state.selected_language = st.selectbox( | |
| "Select output language:", | |
| languages, | |
| index=languages.index(st.session_state.get("selected_language", "English")) | |
| ) | |
| # Option selection | |
| option = st.radio( | |
| "Choose an action:", | |
| ("Ask a general agriculture-related question", "Input sensor data for recommendations", "Satellite Data", "FAQ Section") | |
| ) | |
| # Option 1: Ask AI any agriculture-related question | |
| if option == "Ask a general agriculture-related question": | |
| user_query = st.chat_input("Ask AI anything about agriculture...") | |
| if user_query: | |
| with st.spinner("Processing your query..."): | |
| # Display user's query | |
| with st.chat_message("user"): | |
| st.markdown(user_query) | |
| # Get assistant's response | |
| with st.chat_message("assistant"): | |
| response = st.session_state.conversational_chain({"question": user_query}) | |
| assistant_response = response["answer"] | |
| # Translate response based on selected language | |
| translator = GoogleTranslator(source="en", target=st.session_state.selected_language.lower()) | |
| translated_response = translator.translate(assistant_response) | |
| # Display response in selected language | |
| st.markdown(f"**{st.session_state.selected_language}:** {translated_response}") | |
| # Option 2: Input sensor data for recommendations | |
| elif option == "Input sensor data for recommendations": | |
| st.markdown("### Fetching data from sensors...") | |
| sensor_data = fetch_sensor_data() | |
| if sensor_data: | |
| ph = sensor_data["pH"] | |
| moisture = sensor_data["moisture"] | |
| temperature = sensor_data["temperature"] | |
| air_quality = sensor_data["air_quality"] | |
| st.markdown(f"**Sensor Data:**\n- pH: {ph}\n- Moisture: {moisture}%\n- Temperature: {temperature}°C\n- Air Quality: {air_quality}") | |
| if st.button("Get Recommendations"): | |
| with st.spinner("Analyzing data..."): | |
| # Prepare input query | |
| user_input = f"Recommendations for:\n- pH: {ph}\n- Moisture: {moisture}%\n- Temperature: {temperature}°C\n- Air Quality: {air_quality}" | |
| # Display user's input | |
| with st.chat_message("user"): | |
| st.markdown(user_input) | |
| # Get assistant's response | |
| with st.chat_message("assistant"): | |
| response = st.session_state.conversational_chain({"question": user_input}) | |
| assistant_response = response["answer"] | |
| # Translate response based on selected language | |
| translator = GoogleTranslator(source="en", target=st.session_state.selected_language.lower()) | |
| translated_response = translator.translate(assistant_response) | |
| # Display response in selected language | |
| st.markdown(f"**{st.session_state.selected_language}:** {translated_response}") | |
| # Option 3: Satellite Data | |
| elif option == "Satellite Data": | |
| st.markdown("### Satellite Data Functionality Coming Soon!") | |
| # Option 4: FAQ Section | |
| elif option == "FAQ Section": | |
| st.markdown("### FAQs Coming Soon!") | |
| # OLD code | |
| # import os | |
| # import json | |
| # from datetime import datetime | |
| # import streamlit as st | |
| # from langchain_huggingface import HuggingFaceEmbeddings | |
| # from langchain_chroma import Chroma | |
| # from langchain_groq import ChatGroq | |
| # from langchain.memory import ConversationBufferMemory | |
| # from langchain.chains import ConversationalRetrievalChain | |
| # from deep_translator import GoogleTranslator | |
| # # Directory paths and configurations | |
| # working_dir = os.path.dirname(os.path.abspath(__file__)) | |
| # config_data = json.load(open(f"{working_dir}/config.json")) | |
| # GROQ_API_KEY = config_data["GROQ_API_KEY"] | |
| # os.environ["GROQ_API_KEY"] = GROQ_API_KEY | |
| # # Vectorstore setup | |
| # def setup_vectorstore(): | |
| # embeddings = HuggingFaceEmbeddings() | |
| # vectorstore = Chroma(persist_directory="soil_vectordb", embedding_function=embeddings) | |
| # return vectorstore | |
| # # Chatbot chain setup | |
| # def chat_chain(vectorstore): | |
| # llm = ChatGroq(model="llama-3.1-70b-versatile", temperature=0) | |
| # retriever = vectorstore.as_retriever() | |
| # memory = ConversationBufferMemory( | |
| # llm=llm, | |
| # output_key="answer", | |
| # memory_key="chat_history", | |
| # return_messages=True | |
| # ) | |
| # chain = ConversationalRetrievalChain.from_llm( | |
| # llm=llm, | |
| # retriever=retriever, | |
| # chain_type="stuff", | |
| # memory=memory, | |
| # verbose=True, | |
| # return_source_documents=True | |
| # ) | |
| # return chain | |
| # # Updated Streamlit setup with language selection dropdown | |
| # st.set_page_config(page_title="Soil.Ai", page_icon="🌱", layout="centered") | |
| # st.title("🌱 Soil.Ai - Smart Farming Recommendations") | |
| # st.subheader("AI-driven solutions for modern farming!") | |
| # # Initialize session state | |
| # if "username" not in st.session_state: | |
| # username = st.text_input("Enter your name to proceed:") | |
| # if username: | |
| # with st.spinner("Loading AI interface..."): | |
| # st.session_state.username = username | |
| # st.session_state.vectorstore = setup_vectorstore() | |
| # st.session_state.conversational_chain = chat_chain(st.session_state.vectorstore) | |
| # st.session_state.selected_language = "English" # Default language | |
| # st.success(f"Welcome, {username}! Start by choosing an option.") | |
| # else: | |
| # username = st.session_state.username | |
| # # Language options | |
| # languages = [ | |
| # "English", "Marathi", "Hindi", "Bengali", "Gujarati", "Kannada", "Malayalam", | |
| # "Odia", "Punjabi", "Tamil", "Telugu", "Urdu", "Spanish", "French", "German" | |
| # ] | |
| # # Main interface | |
| # if "conversational_chain" not in st.session_state: | |
| # st.session_state.vectorstore = setup_vectorstore() | |
| # st.session_state.conversational_chain = chat_chain(st.session_state.vectorstore) | |
| # if "username" in st.session_state: | |
| # st.subheader(f"Hello {username}, choose your option below:") | |
| # # Dropdown for selecting output language | |
| # st.session_state.selected_language = st.selectbox( | |
| # "Select output language:", | |
| # languages, | |
| # index=languages.index(st.session_state.get("selected_language", "English")) | |
| # ) | |
| # # Option selection | |
| # option = st.radio( | |
| # "Choose an action:", | |
| # ("Ask a general agriculture-related question", "Input sensor data for recommendations", "Satellite Data", "FAQ Section") | |
| # ) | |
| # # Option 1: Ask AI any agriculture-related question | |
| # if option == "Ask a general agriculture-related question": | |
| # user_query = st.chat_input("Ask AI anything about agriculture...") | |
| # if user_query: | |
| # with st.spinner("Processing your query..."): | |
| # # Display user's query | |
| # with st.chat_message("user"): | |
| # st.markdown(user_query) | |
| # # Get assistant's response | |
| # with st.chat_message("assistant"): | |
| # response = st.session_state.conversational_chain({"question": user_query}) | |
| # assistant_response = response["answer"] | |
| # # Translate response based on selected language | |
| # translator = GoogleTranslator(source="en", target=st.session_state.selected_language.lower()) | |
| # translated_response = translator.translate(assistant_response) | |
| # # Display response in selected language | |
| # st.markdown(f"**{st.session_state.selected_language}:** {translated_response}") | |
| # # Option 2: Input sensor data for recommendations | |
| # elif option == "Input sensor data for recommendations": | |
| # st.markdown("### Enter soil and environmental parameters:") | |
| # ph = st.number_input("Enter Soil pH", min_value=0.0, max_value=14.0, step=0.1) | |
| # moisture = st.number_input("Enter Soil Moisture (%)", min_value=0.0, max_value=100.0, step=0.1) | |
| # temperature = st.number_input("Enter Temperature (°C)", min_value=-50.0, max_value=60.0, step=0.1) | |
| # air_quality = st.number_input("Enter Air Quality Index (AQI)", min_value=0, max_value=500, step=1) | |
| # if st.button("Get Recommendations"): | |
| # if ph and moisture and temperature and air_quality: | |
| # with st.spinner("Analyzing data..."): | |
| # # Prepare input query | |
| # user_input = f"Recommendations for:\n- pH: {ph}\n- Moisture: {moisture}%\n- Temperature: {temperature}°C\n- Air Quality: {air_quality}" | |
| # # Display user's input | |
| # with st.chat_message("user"): | |
| # st.markdown(user_input) | |
| # # Get assistant's response | |
| # with st.chat_message("assistant"): | |
| # response = st.session_state.conversational_chain({"question": user_input}) | |
| # assistant_response = response["answer"] | |
| # # Translate response based on selected language | |
| # translator = GoogleTranslator(source="en", target=st.session_state.selected_language.lower()) | |
| # translated_response = translator.translate(assistant_response) | |
| # # Display response in selected language | |
| # st.markdown(f"**{st.session_state.selected_language}:** {translated_response}") | |
| # else: | |
| # st.error("Please fill in all the fields!") | |
| # # Option 3: Satellite Data | |
| # elif option == "Satellite Data": | |
| # st.markdown("### Satellite Data Functionality Coming Soon!") | |
| # # Option 4: FAQ Section | |
| # elif option == "FAQ Section": | |
| # crop = st.radio("Select a crop for FAQs:", ("Cotton", "Tur")) | |
| # if crop == "Tur": | |
| # st.markdown("### *Q&A on Arhar Crop*") | |
| # tur_questions = [ | |
| # "Q1: What are the suitable climate and soil requirements for Arhar cultivation?", | |
| # "Q2: What is the best time for sowing Arhar, and how much seed is needed per hectare?", | |
| # "Q3: What are the improved varieties of Arhar and their characteristics?", | |
| # "Q4: What fertilizers and irrigation are required for Arhar cultivation?", | |
| # "Q5: What are the main pests and diseases affecting Arhar, and how can they be managed?" | |
| # ] | |
| # tur_answers = [ | |
| # "A: Arhar requires a warm and dry climate with a temperature range of 25-30°C. It thrives in well-drained loamy soil with a pH value of 6.0 to 7.5.", | |
| # "A: The best time for sowing Arhar is from June to July (monsoon season). The seed requirement is 15-20 kg per hectare. The seeds should be treated with Trichoderma or Carbendazim before sowing.", | |
| # "A: Some improved varieties of Arhar include ICPL-87 (early maturing), Sharad (high-yielding), and Pant Arhar-3 (short-duration).", | |
| # "A: Fertilizers: Nitrogen: 20 kg/hectare, Phosphorus: 50 kg/hectare. Irrigation: Two to three irrigations during flowering and pod formation stages.", | |
| # "A: Pests like pod borers and diseases like wilt (root rot) affect Arhar. Control measures include spraying neem oil and using disease-resistant varieties." | |
| # ] | |
| # elif crop == "Cotton": | |
| # st.markdown("### *Q&A on Cotton Crop*") | |
| # tur_questions = [ | |
| # "Q1: What is the suitable climate for cotton cultivation?", | |
| # "Q2: How much water does cotton require during its growth?", | |
| # "Q3: What are the common pests and diseases in cotton?", | |
| # "Q4: Which fertilizers are best for cotton farming?", | |
| # "Q5: What is the average yield of cotton per hectare?" | |
| # ] | |
| # tur_answers = [ | |
| # "A: Cotton grows well in warm climates with temperatures between 21-30°C.", | |
| # "A: Cotton requires about 700-1300 mm of water depending on the variety and climate.", | |
| # "A: Common pests include bollworms; diseases include leaf curl virus.", | |
| # "A: Use nitrogen (60 kg/ha), phosphorus (30 kg/ha), and potassium (30 kg/ha).", | |
| # "A: Average yield ranges between 500-800 kg/ha depending on the variety and conditions." | |
| # ] | |
| # for q, a in zip(tur_questions, tur_answers): | |
| # translator = GoogleTranslator(source="en", target=st.session_state.selected_language.lower()) | |
| # st.markdown(f"**{translator.translate(q)}**\n\n{translator.translate(a)}") | |