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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)}")
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