Update app.py

app.py

@@ -1,5 +1,8 @@
+# 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
@@ -15,6 +18,9 @@ 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()
@@ -41,6 +47,28 @@ def chat_chain(vectorstore):
     )
     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")
@@ -109,14 +137,17 @@ if "username" in st.session_state:
 
     # 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:
+        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}"
@@ -136,8 +167,6 @@ if "username" in st.session_state:
 
                         # 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":
@@ -145,40 +174,201 @@ if "username" in st.session_state:
 
     # 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)}")
+        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)}")