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| import streamlit as st | |
| from PIL import Image | |
| import google.generativeai as genai | |
| from geopy.geocoders import Nominatim | |
| from deep_translator import GoogleTranslator | |
| import requests | |
| import io | |
| import datetime | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| # Get the current date | |
| current_date = datetime.datetime.now() | |
| cur_date = f"{current_date.day}/{current_date.month}/{current_date.year}" | |
| # Configure Google Generative AI | |
| genai.configure(api_key="AIzaSyAXCVMFirOACW80v3w4lNEruWBAskWbeiw") # Replace with your API key | |
| weather_model = genai.GenerativeModel('gemini-1.5-flash') | |
| farming_model = genai.GenerativeModel('gemini-pro') | |
| # Initialize session state | |
| if 'location' not in st.session_state: | |
| st.session_state['location'] = "" | |
| if 'crops' not in st.session_state: | |
| st.session_state['crops'] = "" | |
| if 'section' not in st.session_state: | |
| st.session_state['section'] = "Weather Update" | |
| if 'weather_data' not in st.session_state: | |
| st.session_state['weather_data'] = None | |
| if 'farming_plan' not in st.session_state: | |
| st.session_state['farming_plan'] = None | |
| if 'url' not in st.session_state: | |
| st.session_state['url'] = None | |
| # Sidebar navigation | |
| st.sidebar.title("Menu") | |
| st.sidebar.radio( | |
| "Choose a section:", | |
| ["Edit Information", "Weather Update", "Disease Prediction", "Price Plots"], | |
| key='section' | |
| ) | |
| # Function to get location coordinates | |
| def get_location_coordinates(location_name): | |
| loc = Nominatim(user_agent="GetLoc") | |
| getLoc = loc.geocode(location_name) | |
| if getLoc: | |
| return getLoc.latitude, getLoc.longitude | |
| else: | |
| return None, None | |
| # Function to fetch HTML content from a weather website | |
| def get_html(url): | |
| try: | |
| headers = {"User-Agent": "Mozilla/5.0"} | |
| response = requests.get(url, headers=headers) | |
| if response.status_code == 200: | |
| return response.text | |
| else: | |
| st.error(f"Failed to fetch the page. Status code: {response.status_code}") | |
| return None | |
| except Exception as e: | |
| st.error(f"An error occurred: {e}") | |
| return None | |
| # Function to analyze weather data using AI | |
| def analyze_weather_data(raw_data): | |
| st.info("🔍 Crunching the weather data... Hang tight, farmer!") | |
| prompt = f""" | |
| I have the following text, | |
| {raw_data} | |
| This is a 15-day weather forecast. Today's date is `{cur_date}` in DD/MM/YYYY format. | |
| Please analyze the data and format it correctly in this structure: | |
| Date: {{date}} | |
| Highest Temperature: {{highest_temperature}} | |
| Lowest Temperature: {{lowest_temperature}} | |
| Humidity: {{humidity}} | |
| Percentage of Rain: {{rain_percentage}} | |
| Wind Speed: {{wind_speed}} | |
| Climate: {{climate}} | |
| """ | |
| response = weather_model.generate_content(prompt) | |
| return response.text | |
| # Function to generate a farming plan | |
| def generate_farming_plan(analyzed_data, crops): | |
| st.info("🌾 Mapping out a smart farming plan for you... Stay tuned!") | |
| prompt = f""" | |
| Here is the text from a weather report of next 15 days: | |
| {analyzed_data} | |
| The user is actually a farmer and has these crops: {crops}. | |
| Analyse the data completely and give me a concise farming plan for the next 15 days. | |
| Focus on the varying weather reports, highlight if the farmer wants to undergo any precautions before anything unexpected happens. | |
| Provide it with the actual date. | |
| """ | |
| response = farming_model.generate_content(prompt) | |
| return response.text | |
| # Function to analyze plant disease using AI | |
| def analyze_plant_disease(image_description, image): | |
| st.info("🧐 Investigating plant health... This will just take a moment!") | |
| prompt = """ | |
| Analyze the provided image of a plant and determine the type of disease it is affected by. | |
| Follow the structure outlined below: | |
| Template for Response: | |
| ``` | |
| Plant Name: {plant_name} | |
| Disease Name: {disease_name} | |
| Disease Severity: {severity_level} | |
| Suggestions to Overcome: {suggestions} | |
| ``` | |
| Guidelines: | |
| - Identify the type of disease in the image of the plant. | |
| - If unclear, provide an approximate answer. | |
| - Ensure the response is concise, accurate, and aligned with the template. | |
| """ | |
| if image_description: | |
| prompt += f""" | |
| Here is a description of the image from the user: | |
| ``` | |
| {image_description} | |
| ``` | |
| Consider this information carefully in your response. | |
| """ | |
| img_buffer = io.BytesIO() | |
| image.save(img_buffer, format="PNG") | |
| img_buffer.seek(0) | |
| response = weather_model.generate_content([prompt, Image.open(img_buffer)]) | |
| return response.text.strip("```") | |
| # Function to translate text to Tamil | |
| def translate_to_tamil(text): | |
| translator = GoogleTranslator(source='auto', target='ta') | |
| return translator.translate(text) | |
| def bold_text_before_colon(text): | |
| # Split the input text into lines | |
| lines = text.split('\n') | |
| # Process each line to add bold formatting before the colon | |
| formatted_lines = [] | |
| for line in lines: | |
| if ':' in line: | |
| # Split the line at the first colon | |
| before_colon, after_colon = line.split(':', 1) | |
| # Add bold formatting to the part before the colon | |
| formatted_line = f"**{before_colon.strip()}**: {after_colon.strip()}" | |
| else: | |
| # If no colon is present, keep the line unchanged | |
| formatted_line = line.strip() | |
| formatted_lines.append(formatted_line) | |
| # Join all the formatted lines back together | |
| return '\n'.join(formatted_lines) | |
| def clear_weather_data(): | |
| st.session_state['weather_data'] = None | |
| st.session_state['farming_plan'] = None | |
| # Function to plot individual datasets | |
| def plot_data(data, x_col, y_col, xlabel, ylabel, title): | |
| fig, ax = plt.subplots() | |
| ax.plot(data[x_col], data[y_col]) | |
| ax.set_xlabel(xlabel) | |
| ax.set_ylabel(ylabel) | |
| ax.set_title(title) | |
| st.pyplot(fig) | |
| # Main application logic | |
| if st.session_state['section'] == "Edit Information": | |
| st.title("Edit Information") | |
| location = st.text_input("Enter the location:", st.session_state['location']) | |
| crops = st.text_input("Enter your crops (comma-separated):", st.session_state['crops']) | |
| if st.button("Save"): | |
| st.session_state['location'] = location | |
| st.session_state['crops'] = crops | |
| st.success("Information saved! 🎉") | |
| clear_weather_data() | |
| elif st.session_state['section'] == "Weather Update": | |
| st.title("15-Day Weather Forecast") | |
| if not st.session_state['location'] or not st.session_state['crops']: | |
| st.error("Please enter your location and crops in the 'Edit Information' section.") | |
| else: | |
| if not st.session_state['weather_data']: | |
| lat, long = get_location_coordinates(st.session_state['location']) | |
| if lat and long: | |
| st.session_state['url'] = f"https://weather.com/en-IN/weather/tenday/l/{lat},{long}?par=google" | |
| html_content = get_html(st.session_state['url']) | |
| if html_content: | |
| st.markdown("⚡ **Analyzing weather trends with cutting-edge AI...**") | |
| weather_data = analyze_weather_data(html_content) | |
| farming_plan = generate_farming_plan(weather_data, st.session_state['crops']) | |
| st.session_state['weather_data'] = weather_data | |
| st.session_state['farming_plan'] = farming_plan | |
| else: | |
| st.error("Location not found. Please enter a valid location.") | |
| st.markdown(st.session_state['farming_plan']) | |
| st.markdown(f"[For more weather updates, click here]({st.session_state['url']})") | |
| elif st.session_state['section'] == "Disease Prediction": | |
| st.title("Plant Disease Detection System") | |
| uploaded_image = st.file_uploader("Upload a plant image", type=["jpg", "jpeg", "png"]) | |
| if uploaded_image: | |
| image = Image.open(uploaded_image) | |
| st.image(image, caption="Uploaded Image", use_column_width=True) | |
| image_description = st.text_area("Enter a brief description of the plant and symptoms (optional):", "") | |
| if st.button("Analyze"): | |
| with st.spinner("Analyzing the plant disease..."): | |
| original_response = bold_text_before_colon(analyze_plant_disease(image_description, image)) | |
| st.success("Analysis Complete! 🎯") | |
| st.subheader("**Analysis Report (English):**") | |
| original_response = original_response.replace("\n"," \n").replace("\n\n"," \n") | |
| st.markdown(original_response) | |
| tamil_translation = translate_to_tamil(original_response) | |
| st.subheader("**Analysis Report (Tamil):**") | |
| tamil_translation = tamil_translation.replace("\n"," \n").replace("\n\n"," \n") | |
| st.markdown(tamil_translation) | |
| elif st.session_state['section'] == "Price Plots": | |
| st.title("Crop Price Trends") | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| df = pd.read_csv("content/retail_prices.csv") | |
| st.subheader("Rice Prices") | |
| if not df.empty: | |
| plot_data(df, 'date', 'pricw', 'Date', 'Price of Rice', 'Date vs Price of Rice') | |
| else: | |
| st.write("No data available for Rice.") | |
| with col2: | |
| df1 = pd.read_csv("content/Tomato.csv") | |
| st.subheader("Tomato Prices") | |
| if not df1.empty: | |
| plot_data(df1, 'Date', 'Price', 'Date', 'Price of Tomato', 'Date vs Price of Tomato') | |
| else: | |
| st.write("No data available for Tomato.") | |
| col3, col4 = st.columns(2) | |
| with col3: | |
| df3 = pd.read_csv("content/turmeric_price_6_months_with_dates.csv") | |
| st.subheader("Turmeric Prices") | |
| if not df3.empty: | |
| plot_data(df3, 'Date', 'Price', 'Date', 'Price of Turmeric', 'Date vs Price of Turmeric') | |
| else: | |
| st.write("No data available for Turmeric.") | |
| with col4: | |
| df4 = pd.read_csv("content/44444.csv") | |
| st.subheader("Wheat Prices") | |
| if not df4.empty: | |
| plot_data(df4, 'date', 'pricw', 'Date', 'Price of Wheat', 'Date vs Price of Wheat') | |
| else: | |
| st.write("No data available for Wheat.") |