Update app.py

app.py

@@ -1,6 +1,5 @@
 import os
 import json
-import sqlite3
 from datetime import datetime
 import streamlit as st
 from langchain_huggingface import HuggingFaceEmbeddings
@@ -16,32 +15,6 @@ 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
 
-# Database setup
-def setup_db():
-    conn = sqlite3.connect("chat_history.db", check_same_thread=False)
-    cursor = conn.cursor()
-    cursor.execute("""
-        CREATE TABLE IF NOT EXISTS chat_histories (
-            id INTEGER PRIMARY KEY AUTOINCREMENT,
-            username TEXT,
-            timestamp TEXT,
-            day TEXT,
-            user_input TEXT,
-            assistant_response TEXT
-        )
-    """)
-    conn.commit()
-    return conn
-
-# Save chat history to SQLite
-def save_chat_history(conn, username, timestamp, day, user_input, assistant_response):
-    cursor = conn.cursor()
-    cursor.execute("""
-        INSERT INTO chat_histories (username, timestamp, day, user_input, assistant_response)
-        VALUES (?, ?, ?, ?, ?)
-    """, (username, timestamp, day, user_input, assistant_response))
-    conn.commit()
-
 # Vectorstore setup
 def setup_vectorstore():
     embeddings = HuggingFaceEmbeddings()
@@ -68,22 +41,17 @@ def chat_chain(vectorstore):
     )
     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 database and session state
-if "conn" not in st.session_state:
-    st.session_state.conn = setup_db()
-
+# 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.chat_history = []
             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
@@ -91,6 +59,12 @@ if "username" not in st.session_state:
 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()
@@ -102,14 +76,14 @@ if "username" in st.session_state:
     # Dropdown for selecting output language
     st.session_state.selected_language = st.selectbox(
         "Select output language:",
-        ("English", "Marathi", "Hindi"),
-        index=["English", "Marathi", "Hindi"].index(st.session_state.get("selected_language", "English"))
+        languages,
+        index=languages.index(st.session_state.get("selected_language", "English"))
     )
 
-    # Option selection: Ask a general question or input sensor data
+    # Option selection
     option = st.radio(
         "Choose an action:",
-        ("Ask a general agriculture-related question", "Input sensor data for recommendations")
+        ("Ask a general agriculture-related question", "Input sensor data for recommendations", "Satellite Data", "FAQ Section")
     )
 
     # Option 1: Ask AI any agriculture-related question
@@ -127,27 +101,12 @@ if "username" in st.session_state:
                     assistant_response = response["answer"]
 
                     # Translate response based on selected language
-                    if st.session_state.selected_language == "Marathi":
-                        translator = GoogleTranslator(source="en", target="mr")
-                        translated_response = translator.translate(assistant_response)
-                    elif st.session_state.selected_language == "Hindi":
-                        translator = GoogleTranslator(source="en", target="hi")
-                        translated_response = translator.translate(assistant_response)
-                    else:  # Default: English
-                        translated_response = assistant_response
+                    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}")
 
-                    # Save chat history
-                    st.session_state.chat_history.append({"role": "user", "content": user_query})
-                    st.session_state.chat_history.append({"role": "assistant", "content": assistant_response})
-
-                    # Save to database
-                    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-                    day = datetime.now().strftime("%A")
-                    save_chat_history(st.session_state.conn, username, timestamp, day, user_query, assistant_response)
-
     # Option 2: Input sensor data for recommendations
     elif option == "Input sensor data for recommendations":
         st.markdown("### Enter soil and environmental parameters:")
@@ -172,25 +131,54 @@ if "username" in st.session_state:
                         assistant_response = response["answer"]
 
                         # Translate response based on selected language
-                        if st.session_state.selected_language == "Marathi":
-                            translator = GoogleTranslator(source="en", target="mr")
-                            translated_response = translator.translate(assistant_response)
-                        elif st.session_state.selected_language == "Hindi":
-                            translator = GoogleTranslator(source="en", target="hi")
-                            translated_response = translator.translate(assistant_response)
-                        else:  # Default: English
-                            translated_response = assistant_response
+                        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}")
-
-                        # Save chat history
-                        st.session_state.chat_history.append({"role": "user", "content": user_input})
-                        st.session_state.chat_history.append({"role": "assistant", "content": assistant_response})
-
-                        # Save to database
-                        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-                        day = datetime.now().strftime("%A")
-                        save_chat_history(st.session_state.conn, username, timestamp, day, user_input, assistant_response)
             else:
-                st.error("Please fill in all the fields!")
+                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)}")