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import streamlit as st |
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from groq import Groq |
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import requests |
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import pandas as pd |
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from datetime import datetime, timedelta |
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import pycountry |
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from fpdf import FPDF |
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import io |
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import base64 |
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from geopy.geocoders import Nominatim |
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from geopy.exc import GeocoderTimedOut |
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import plotly.express as px |
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import plotly.graph_objects as go |
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import unicodedata |
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import os |
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from dotenv import load_dotenv |
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load_dotenv() |
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GROQ_API_KEY = os.getenv("GROQ_API_KEY") |
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AIRVISUAL_API_KEY = os.getenv("AIRVISUAL_API_KEY") |
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DEFAULT_MODEL = "llama3-70b-8192" |
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client = Groq(api_key=GROQ_API_KEY) |
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st.set_page_config( |
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page_title="🌱 AI Climate & Smart Farming Assistant", |
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page_icon="🌾", |
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layout="wide", |
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initial_sidebar_state="expanded" |
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) |
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st.markdown( |
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""" |
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<style> |
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.main { |
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background-color: #f9f9f9; |
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color: #222; |
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font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; |
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} |
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.title { |
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text-align: center; |
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color: #2E7D32; |
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font-weight: 800; |
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} |
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.subtitle { |
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text-align: center; |
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font-size: 18px; |
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margin-bottom: 20px; |
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color: #4CAF50; |
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} |
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.history-box { |
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background-color: #e8f5e9; |
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padding: 10px; |
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margin-bottom: 10px; |
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border-radius: 8px; |
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border-left: 5px solid #66bb6a; |
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color: #000000; |
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} |
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.ai-response { |
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background-color: #c8e6c9; |
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padding: 10px; |
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margin-bottom: 15px; |
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border-radius: 10px; |
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white-space: pre-wrap; |
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color: #000000; |
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} |
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.user-input { |
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background-color: #dcedc8; |
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padding: 8px; |
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border-radius: 8px; |
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font-weight: bold; |
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margin-bottom: 5px; |
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color: #000000; |
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} |
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.download-button { |
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background-color: #4CAF50; |
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color: white; |
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padding: 10px 20px; |
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border-radius: 5px; |
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text-decoration: none; |
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display: inline-block; |
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margin: 10px 0; |
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} |
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.insight-box { |
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background-color: #e1f5fe; |
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padding: 15px; |
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border-radius: 10px; |
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margin: 15px 0; |
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border-left: 4px solid #0288d1; |
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color: #000000; |
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font-weight: 500; |
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line-height: 1.6; |
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} |
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</style> |
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""", |
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unsafe_allow_html=True |
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) |
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st.markdown("<h1 class='title'>🌾 AI Climate & Smart Farming Assistant</h1>", unsafe_allow_html=True) |
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st.markdown("<p class='subtitle'>Real-time AI insights + live weather data</p>", unsafe_allow_html=True) |
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st.markdown("---") |
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system_prompts = { |
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"Track Pollution": ( |
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"You are an expert environmental scientist. " |
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"Help users understand pollution levels in air, water, or soil using scientific reasoning. " |
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"Provide actionable recommendations for improvement." |
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), |
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"Carbon Emissions": ( |
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"You are a sustainability advisor. " |
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"Estimate and explain carbon emissions, suggest reductions and eco-friendly alternatives. " |
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"Include cost-benefit analysis and ROI calculations." |
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), |
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"Predict Climate Patterns": ( |
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"You are a climate researcher. Predict or explain regional climate changes using current and historical data. " |
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"Include statistical analysis and confidence intervals." |
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), |
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"Smart Farming Advice": ( |
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"You are an AI-powered farming assistant. Help users with crop selection, irrigation, pest control, and yield optimization. " |
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"Focus on sustainable practices and resource efficiency." |
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), |
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} |
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example_queries = { |
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"Track Pollution": "e.g., What's the air quality near Lahore right now?", |
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"Carbon Emissions": "e.g., How can a factory reduce CO2 output sustainably?", |
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"Predict Climate Patterns": "e.g., What climate changes are expected in sub-Saharan Africa?", |
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"Smart Farming Advice": "e.g., Best crops to grow in dry conditions in Uganda?", |
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} |
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def get_weather(location: str): |
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try: |
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geocoding_url = f"https://geocoding-api.open-meteo.com/v1/search?name={location}&count=1" |
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geo_resp = requests.get(geocoding_url, timeout=10) |
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geo_resp.raise_for_status() |
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geo_data = geo_resp.json() |
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if not geo_data.get('results'): |
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return None |
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lat = geo_data['results'][0]['latitude'] |
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lon = geo_data['results'][0]['longitude'] |
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location_name = geo_data['results'][0]['name'] |
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weather_url = f"https://api.open-meteo.com/v1/forecast?latitude={lat}&longitude={lon}¤t=temperature_2m,relative_humidity_2m,wind_speed_10m,weather_code" |
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weather_resp = requests.get(weather_url, timeout=10) |
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weather_resp.raise_for_status() |
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weather_data = weather_resp.json() |
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weather_codes = { |
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0: "Clear sky", |
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1: "Mainly clear", |
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2: "Partly cloudy", |
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3: "Overcast", |
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45: "Foggy", |
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48: "Depositing rime fog", |
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51: "Light drizzle", |
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53: "Moderate drizzle", |
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55: "Dense drizzle", |
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61: "Slight rain", |
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63: "Moderate rain", |
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65: "Heavy rain", |
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71: "Slight snow", |
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73: "Moderate snow", |
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75: "Heavy snow", |
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77: "Snow grains", |
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80: "Slight rain showers", |
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81: "Moderate rain showers", |
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82: "Violent rain showers", |
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85: "Slight snow showers", |
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86: "Heavy snow showers", |
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95: "Thunderstorm", |
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96: "Thunderstorm with slight hail", |
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99: "Thunderstorm with heavy hail" |
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} |
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current = weather_data['current'] |
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weather_code = current['weather_code'] |
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weather_desc = weather_codes.get(weather_code, "Unknown") |
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return { |
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"location": location_name, |
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"description": weather_desc, |
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"temperature_C": current['temperature_2m'], |
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"humidity_%": current['relative_humidity_2m'], |
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"wind_speed_m/s": current['wind_speed_10m'] |
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} |
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except Exception as e: |
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return None |
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def get_historical_weather(location: str, days: int = 7): |
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try: |
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geocoding_url = f"https://geocoding-api.open-meteo.com/v1/search?name={location}&count=1" |
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geo_resp = requests.get(geocoding_url, timeout=10) |
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geo_resp.raise_for_status() |
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geo_data = geo_resp.json() |
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if not geo_data.get('results'): |
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return None |
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lat = geo_data['results'][0]['latitude'] |
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lon = geo_data['results'][0]['longitude'] |
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end_date = datetime.now() |
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start_date = end_date - timedelta(days=days) |
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weather_url = ( |
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f"https://api.open-meteo.com/v1/forecast" |
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f"?latitude={lat}&longitude={lon}" |
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f"&start_date={start_date.strftime('%Y-%m-%d')}" |
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f"&end_date={end_date.strftime('%Y-%m-%d')}" |
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f"&daily=temperature_2m_max,temperature_2m_min,precipitation_sum,wind_speed_10m_max" |
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) |
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weather_resp = requests.get(weather_url, timeout=10) |
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weather_resp.raise_for_status() |
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return weather_resp.json() |
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except Exception as e: |
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return None |
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def get_air_quality(location: str): |
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try: |
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geocoding_url = f"https://geocoding-api.open-meteo.com/v1/search?name={location}&count=1" |
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geo_resp = requests.get(geocoding_url, timeout=10) |
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geo_resp.raise_for_status() |
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geo_data = geo_resp.json() |
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if not geo_data.get('results'): |
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return None |
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lat = geo_data['results'][0]['latitude'] |
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lon = geo_data['results'][0]['longitude'] |
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aq_url = f"https://air-quality-api.open-meteo.com/v1/air-quality?latitude={lat}&longitude={lon}¤t=pm10,pm2_5,ozone,nitrogen_dioxide,sulphur_dioxide" |
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aq_resp = requests.get(aq_url, timeout=10) |
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if aq_resp.status_code == 200: |
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aq_data = aq_resp.json() |
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if 'current' in aq_data: |
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return aq_data |
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airvisual_url = f"http://api.airvisual.com/v2/nearest_city?lat={lat}&lon={lon}&key={AIRVISUAL_API_KEY}" |
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airvisual_resp = requests.get(airvisual_url, timeout=10) |
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if airvisual_resp.status_code == 200: |
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airvisual_data = airvisual_resp.json() |
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if 'data' in airvisual_data and 'current' in airvisual_data['data']: |
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current = airvisual_data['data']['current']['pollution'] |
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return { |
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'current': { |
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'pm10': current.get('p1'), |
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'pm2_5': current.get('p2'), |
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'ozone': current.get('o3'), |
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'nitrogen_dioxide': None, |
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'sulphur_dioxide': None |
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} |
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} |
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return None |
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except Exception as e: |
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print(f"Air quality error: {str(e)}") |
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return None |
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def clean_text_for_pdf(text): |
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"""Clean text to be PDF-safe by removing or replacing problematic characters""" |
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text = unicodedata.normalize('NFKD', text) |
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replacements = { |
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'μ': 'micro', |
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'°': ' degrees', |
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'℃': 'C', |
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'±': '+/-', |
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'×': 'x', |
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'÷': '/', |
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'≤': '<=', |
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'≥': '>=', |
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'≠': '!=', |
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'∞': 'infinity', |
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'→': '->', |
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'←': '<-', |
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'↑': 'up', |
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'↓': 'down', |
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'↔': '<->', |
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'≈': '~=', |
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'∑': 'sum', |
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'∏': 'product', |
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'√': 'sqrt', |
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'∫': 'integral', |
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'∆': 'delta', |
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'∇': 'nabla', |
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'∂': 'partial', |
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'∝': 'proportional to', |
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'∞': 'infinity', |
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'∅': 'empty set', |
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'∈': 'in', |
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'∉': 'not in', |
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'⊂': 'subset', |
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'⊃': 'superset', |
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'∪': 'union', |
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'∩': 'intersection', |
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'∀': 'for all', |
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'∃': 'exists', |
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'∄': 'does not exist', |
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'∴': 'therefore', |
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'∵': 'because' |
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} |
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for char, replacement in replacements.items(): |
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text = text.replace(char, replacement) |
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return text |
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def generate_pdf(chat_history, title="AI Climate & Farming Advice"): |
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pdf = FPDF() |
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pdf.add_page() |
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pdf.set_font("helvetica", "B", 16) |
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pdf.cell(0, 10, clean_text_for_pdf(title), ln=True, align='C') |
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pdf.ln(10) |
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for chat in chat_history: |
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pdf.set_font("helvetica", "B", 12) |
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pdf.cell(0, 10, "User:", ln=True) |
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pdf.set_font("helvetica", "", 12) |
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user_text = clean_text_for_pdf(chat["user"]) |
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pdf.multi_cell(0, 10, user_text) |
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pdf.ln(5) |
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pdf.set_font("helvetica", "B", 12) |
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pdf.cell(0, 10, "AI Response:", ln=True) |
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pdf.set_font("helvetica", "", 12) |
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ai_text = clean_text_for_pdf(chat["ai"]) |
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pdf.multi_cell(0, 10, ai_text) |
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pdf.ln(10) |
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return pdf.output(dest="S").encode("latin-1", "replace") |
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def get_country_list(): |
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countries = [country.name for country in pycountry.countries] |
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return sorted(countries) |
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st.sidebar.header("🌟 Features") |
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page = st.sidebar.radio( |
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"Choose your tool:", |
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[ |
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"AI Assistant Chat", |
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"Weather Data", |
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"Smart Farming CSV Analysis", |
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] |
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) |
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if page == "AI Assistant Chat": |
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st.subheader("🧠 AI Climate & Farming Chat Assistant") |
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option = st.selectbox( |
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"Choose a use case:", |
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list(system_prompts.keys()) |
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) |
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st.markdown(f"💡 *Example*: {example_queries[option]}") |
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user_input = st.text_area("Enter your question or describe your situation:") |
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if "chat_history" not in st.session_state: |
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st.session_state.chat_history = [] |
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if st.button("Send to AI") and user_input.strip(): |
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with st.spinner("Thinking..."): |
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messages = [ |
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{"role": "system", "content": system_prompts[option]}, |
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] |
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for chat in st.session_state.chat_history: |
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messages.append({"role": "user", "content": chat["user"]}) |
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messages.append({"role": "assistant", "content": chat["ai"]}) |
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messages.append({"role": "user", "content": user_input}) |
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response = client.chat.completions.create( |
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model=DEFAULT_MODEL, |
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messages=messages, |
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) |
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ai_response = response.choices[0].message.content |
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st.session_state.chat_history.append({"user": user_input, "ai": ai_response}) |
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st.rerun() |
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if st.session_state.chat_history: |
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st.markdown("### 🕘 Conversation History") |
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for chat in reversed(st.session_state.chat_history): |
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st.markdown(f"<div class='user-input'>You:</div><div>{chat['user']}</div>", unsafe_allow_html=True) |
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st.markdown(f"<div class='ai-response'>{chat['ai']}</div>", unsafe_allow_html=True) |
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if st.button("Download Chat as PDF"): |
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pdf_bytes = generate_pdf(st.session_state.chat_history) |
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st.download_button( |
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label="Click to Download PDF", |
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data=pdf_bytes, |
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file_name="climate_advice.pdf", |
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mime="application/pdf" |
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) |
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if st.button("Clear Chat History"): |
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st.session_state.chat_history = [] |
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st.rerun() |
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elif page == "Weather Data": |
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st.subheader("🌍 Advanced Weather & Environmental Data") |
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location_method = st.radio( |
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"Choose location input method:", |
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["Enter City", "Select Country"] |
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) |
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location = None |
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if location_method == "Enter City": |
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location = st.text_input("Enter a city or location (e.g., Los Angeles, Delhi):") |
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elif location_method == "Select Country": |
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country = st.selectbox("Select a country:", get_country_list()) |
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city = st.text_input("Enter city name:") |
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location = f"{city}, {country}" if city else None |
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if location: |
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tab1, tab2, tab3 = st.tabs(["Current Weather", "Historical Data", "Air Quality"]) |
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with tab1: |
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if st.button("Get Current Weather"): |
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with st.spinner("Fetching data..."): |
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weather_data = get_weather(location) |
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if weather_data is None: |
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st.error("Failed to fetch weather data for this location.") |
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else: |
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col1, col2 = st.columns(2) |
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with col1: |
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st.markdown(f"### Current Weather in {weather_data['location']}:") |
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st.write(f"- Description: {weather_data['description']}") |
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st.write(f"- Temperature: {weather_data['temperature_C']} °C") |
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st.write(f"- Humidity: {weather_data['humidity_%']} %") |
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st.write(f"- Wind Speed: {weather_data['wind_speed_m/s']} m/s") |
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with col2: |
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fig = go.Figure() |
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fig.add_trace(go.Indicator( |
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|
mode="gauge+number", |
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|
value=weather_data['temperature_C'], |
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|
title={'text': "Temperature (°C)"}, |
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|
gauge={'axis': {'range': [-20, 40]}, |
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'bar': {'color': "darkgreen"}} |
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)) |
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st.plotly_chart(fig) |
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|
|
with tab2: |
|
|
days = st.slider("Select number of days for historical data:", 1, 30, 7) |
|
|
if st.button("Get Historical Weather"): |
|
|
with st.spinner("Fetching historical data..."): |
|
|
hist_data = get_historical_weather(location, days) |
|
|
if hist_data is None: |
|
|
st.error("Failed to fetch historical weather data.") |
|
|
else: |
|
|
daily = hist_data['daily'] |
|
|
df = pd.DataFrame({ |
|
|
'Date': pd.date_range(start=daily['time'][0], periods=len(daily['time'])), |
|
|
'Max Temp': daily['temperature_2m_max'], |
|
|
'Min Temp': daily['temperature_2m_min'], |
|
|
'Precipitation': daily['precipitation_sum'], |
|
|
'Wind Speed': daily['wind_speed_10m_max'] |
|
|
}) |
|
|
|
|
|
|
|
|
fig = go.Figure() |
|
|
fig.add_trace(go.Scatter( |
|
|
x=df['Date'], |
|
|
y=df['Max Temp'], |
|
|
name='Max Temperature', |
|
|
line=dict(color='red') |
|
|
)) |
|
|
fig.add_trace(go.Scatter( |
|
|
x=df['Date'], |
|
|
y=df['Min Temp'], |
|
|
name='Min Temperature', |
|
|
line=dict(color='blue'), |
|
|
fill='tonexty' |
|
|
)) |
|
|
fig.update_layout( |
|
|
title='Temperature Range Over Time', |
|
|
xaxis_title='Date', |
|
|
yaxis_title='Temperature (°C)', |
|
|
hovermode='x unified' |
|
|
) |
|
|
st.plotly_chart(fig) |
|
|
|
|
|
|
|
|
fig2 = go.Figure() |
|
|
fig2.add_trace(go.Bar( |
|
|
x=df['Date'], |
|
|
y=df['Precipitation'], |
|
|
name='Precipitation', |
|
|
marker_color='lightblue' |
|
|
)) |
|
|
fig2.add_trace(go.Scatter( |
|
|
x=df['Date'], |
|
|
y=df['Wind Speed'], |
|
|
name='Wind Speed', |
|
|
line=dict(color='orange'), |
|
|
yaxis='y2' |
|
|
)) |
|
|
fig2.update_layout( |
|
|
title='Precipitation and Wind Speed', |
|
|
xaxis_title='Date', |
|
|
yaxis_title='Precipitation (mm)', |
|
|
yaxis2=dict( |
|
|
title='Wind Speed (m/s)', |
|
|
overlaying='y', |
|
|
side='right' |
|
|
) |
|
|
) |
|
|
st.plotly_chart(fig2) |
|
|
|
|
|
with tab3: |
|
|
if st.button("Get Air Quality Data"): |
|
|
with st.spinner("Fetching air quality data..."): |
|
|
aq_data = get_air_quality(location) |
|
|
if aq_data is None: |
|
|
st.error("Failed to fetch air quality data.") |
|
|
else: |
|
|
st.markdown(f"### Air Quality in {location}") |
|
|
current = aq_data['current'] |
|
|
|
|
|
|
|
|
col1, col2, col3 = st.columns(3) |
|
|
|
|
|
|
|
|
params = { |
|
|
'pm10': {'name': 'PM10 (μg/m³)', 'range': [0, 100]}, |
|
|
'pm2_5': {'name': 'PM2.5 (μg/m³)', 'range': [0, 50]}, |
|
|
'ozone': {'name': 'Ozone (μg/m³)', 'range': [0, 100]}, |
|
|
'nitrogen_dioxide': {'name': 'Nitrogen Dioxide (μg/m³)', 'range': [0, 100]}, |
|
|
'sulphur_dioxide': {'name': 'Sulphur Dioxide (μg/m³)', 'range': [0, 100]} |
|
|
} |
|
|
|
|
|
|
|
|
for i, param in enumerate(['pm2_5', 'pm10', 'ozone']): |
|
|
if param in current and current[param] is not None: |
|
|
with [col1, col2, col3][i]: |
|
|
fig = go.Figure(go.Indicator( |
|
|
mode="gauge+number", |
|
|
value=current[param], |
|
|
title={'text': params[param]['name']}, |
|
|
gauge={'axis': {'range': params[param]['range']}, |
|
|
'bar': {'color': "darkgreen"}} |
|
|
)) |
|
|
st.plotly_chart(fig) |
|
|
|
|
|
|
|
|
st.markdown("### Other Pollutants") |
|
|
col1, col2 = st.columns(2) |
|
|
with col1: |
|
|
if 'nitrogen_dioxide' in current and current['nitrogen_dioxide'] is not None: |
|
|
st.write(f"- Nitrogen Dioxide: {current['nitrogen_dioxide']} μg/m³") |
|
|
with col2: |
|
|
if 'sulphur_dioxide' in current and current['sulphur_dioxide'] is not None: |
|
|
st.write(f"- Sulphur Dioxide: {current['sulphur_dioxide']} μg/m³") |
|
|
|
|
|
|
|
|
elif page == "Smart Farming CSV Analysis": |
|
|
st.subheader("🌱 AI-Powered Farming Data Analysis") |
|
|
uploaded_file = st.file_uploader("Upload your farming dataset (CSV)", type=["csv"]) |
|
|
|
|
|
if uploaded_file: |
|
|
try: |
|
|
df = pd.read_csv(uploaded_file) |
|
|
st.success("✅ Data loaded successfully!") |
|
|
|
|
|
|
|
|
tab1, tab2 = st.tabs(["Data Explorer", "AI Insights"]) |
|
|
|
|
|
with tab1: |
|
|
st.markdown("### Dataset Preview") |
|
|
st.dataframe(df.head(5)) |
|
|
|
|
|
if st.checkbox("Show Summary Statistics"): |
|
|
st.markdown("### Summary Statistics") |
|
|
st.write(df.describe().transpose()) |
|
|
|
|
|
|
|
|
numeric_cols = df.select_dtypes(include=["float64", "int64"]).columns.tolist() |
|
|
if numeric_cols: |
|
|
col1, col2 = st.columns(2) |
|
|
with col1: |
|
|
x_axis = st.selectbox("X-Axis", numeric_cols) |
|
|
with col2: |
|
|
y_axis = st.selectbox("Y-Axis", numeric_cols) |
|
|
|
|
|
if x_axis and y_axis: |
|
|
fig = px.scatter( |
|
|
df, |
|
|
x=x_axis, |
|
|
y=y_axis, |
|
|
title=f"{y_axis} vs {x_axis}", |
|
|
trendline="ols", |
|
|
color_discrete_sequence=["#2E7D32"] |
|
|
) |
|
|
st.plotly_chart(fig) |
|
|
|
|
|
|
|
|
if len(numeric_cols) > 1: |
|
|
st.markdown("### Correlation Matrix") |
|
|
corr = df[numeric_cols].corr() |
|
|
fig = px.imshow(corr, |
|
|
text_auto=True, |
|
|
aspect="auto", |
|
|
color_continuous_scale="Greens") |
|
|
st.plotly_chart(fig) |
|
|
|
|
|
with tab2: |
|
|
st.markdown("### AI-Powered Farming Insights") |
|
|
st.info("Ask specific questions about your farming data to get actionable insights") |
|
|
|
|
|
analysis_prompt = st.text_area( |
|
|
"What insights would you like? (Examples below):", |
|
|
"Analyze this farming data and provide key insights:", |
|
|
height=100 |
|
|
) |
|
|
|
|
|
st.caption("Examples: 'Suggest optimal crops for this region', 'Identify yield patterns', " |
|
|
"'Recommend irrigation improvements', 'Predict harvest timing'") |
|
|
|
|
|
if st.button("Generate AI Insights", type="primary"): |
|
|
with st.spinner("🧠 Analyzing with AI..."): |
|
|
|
|
|
context = f"Dataset has {len(df)} rows and columns: {', '.join(df.columns)}\n" |
|
|
context += f"First 3 rows:\n{df.head(3).to_string(index=False)}" |
|
|
|
|
|
|
|
|
messages = [ |
|
|
{ |
|
|
"role": "system", |
|
|
"content": ( |
|
|
"You are an expert agricultural data scientist. Analyze farming datasets and provide: " |
|
|
"1. Actionable insights for improving crop yield " |
|
|
"2. Recommendations based on climate patterns " |
|
|
"3. Resource optimization strategies " |
|
|
"4. Sustainable farming practices " |
|
|
"Use bullet points and specific numbers when possible." |
|
|
) |
|
|
}, |
|
|
{ |
|
|
"role": "user", |
|
|
"content": f"{analysis_prompt}\n\n{context}" |
|
|
} |
|
|
] |
|
|
|
|
|
response = client.chat.completions.create( |
|
|
model=DEFAULT_MODEL, |
|
|
messages=messages, |
|
|
temperature=0.3 |
|
|
) |
|
|
insights = response.choices[0].message.content |
|
|
st.markdown(f"<div class='insight-box'>{insights}</div>", unsafe_allow_html=True) |
|
|
|
|
|
except Exception as e: |
|
|
st.error(f"❌ Error processing data: {str(e)}") |
|
|
else: |
|
|
st.info("👆 Upload a CSV file containing your farming data to get started") |
|
|
|
|
|
|
|
|
st.markdown("---") |
|
|
st.markdown( |
|
|
"<small>🔋 Powered by <b>llama3-70b-8192</b> on Groq • Real-time data from Open-Meteo API</small>", |
|
|
unsafe_allow_html=True |
|
|
) |
