Add application file

app.py

@@ -0,0 +1,273 @@
+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.")

content/44444.csv

@@ -0,0 +1,55 @@
+date,pricw
+01-03-2018,34.49
+01-04-2018,36.13
+01-05-2018,40.09
+01-06-2018,37.27
+01-07-2018,34.48
+01-08-2018,32.09
+01-09-2018,31.46
+01-10-2018,31.97
+01-11-2018,29.27
+01-12-2018,29.9
+01-01-2019,33
+01-02-2019,31.33
+01-03-2019,31.36
+01-04-2019,28.48
+01-05-2019,30.15
+01-06-2019,28.56
+01-07-2019,27.99
+01-08-2019,29.06
+01-09-2019,32.08
+01-10-2019,36.74
+01-11-2019,39.21
+01-12-2019,43.29
+01-01-2020,34.66
+01-02-2020,38
+01-03-2020,38
+01-05-2020,36
+01-06-2020,40.19
+01-07-2020,38.97
+01-08-2020,39.16
+01-09-2020,39.85
+01-01-2021,50.93
+01-02-2021,47.15
+01-03-2021,48.65
+01-04-2021,53.84
+01-05-2021,57.56
+01-06-2021,67.5
+01-07-2021,61.77
+01-08-2021,59.95
+01-09-2021,50.22
+01-10-2021,64.68
+01-11-2021,60.03
+01-12-2021,62.5
+01-01-2022,68.14
+01-02-2022,71.16
+01-03-2022,63.79
+01-04-2022,51.24
+01-05-2022,57.29
+01-06-2022,54.14
+01-07-2022,50.07
+01-08-2022,49.24
+01-09-2022,48.44
+01-10-2022,34.78
+01-11-2022,32.28
+01-12-2022,32.2

content/Tomato.csv

@@ -0,0 +1,51 @@
+Date,Price
+25-03-2021,35
+26-03-2021,35
+27-03-2021,45
+28-03-2021,45
+29-03-2021,47.5
+30-03-2021,47.5
+31-03-2021,47.5
+01-04-2021,35
+02-04-2021,35
+03-04-2021,40
+04-04-2021,47.5
+05-04-2021,35
+06-04-2021,32.5
+07-04-2021,35
+08-04-2021,35
+09-04-2021,35
+10-04-2021,35
+11-04-2021,30
+12-04-2021,30
+13-04-2021,22.5
+14-04-2021,22.5
+15-04-2021,17.5
+16-04-2021,17.5
+17-04-2021,14
+18-04-2021,17.5
+19-04-2021,12.5
+20-04-2021,12.5
+21-04-2021,17.5
+22-04-2021,12.5
+23-04-2021,16
+24-04-2021,12.5
+25-04-2021,17.5
+26-04-2021,12.5
+27-04-2021,12.5
+28-04-2021,17.5
+29-04-2021,35
+30-04-2021,22.5
+01-05-2021,22.5
+02-05-2021,27.5
+03-05-2021,17.5
+04-05-2021,12.5
+05-05-2021,12.5
+06-05-2021,12.5
+07-05-2021,17.5
+08-05-2021,12.5
+09-05-2021,12.5
+10-05-2021,12.5
+11-05-2021,12.5
+12-05-2021,32.5
+13-05-2021,37.5

content/retail_prices.csv

@@ -0,0 +1,141 @@
+date,pricw
+01-01-2011,60.95
+01-02-2011,63.03
+01-03-2011,62.6
+01-04-2011,62.35
+01-05-2011,62.33
+01-06-2011,62.26
+01-07-2011,61.88
+01-08-2011,60.83
+01-09-2011,58.39
+01-10-2011,58.78
+01-11-2011,60.51
+01-12-2011,62.54
+01-01-2012,61.54
+01-02-2012,59.53
+01-03-2012,58.66
+01-04-2012,57.64
+01-05-2012,57.07
+01-06-2012,58.04
+01-07-2012,59.48
+01-08-2012,58.91
+01-09-2012,61.44
+01-10-2012,61.98
+01-11-2012,62.72
+01-12-2012,63.58
+01-01-2013,63.81
+01-02-2013,63.15
+01-03-2013,62.88
+01-04-2013,63.3
+01-05-2013,63.64
+01-06-2013,63.01
+01-07-2013,62.92
+01-08-2013,62.61
+01-09-2013,62.69
+01-10-2013,63.29
+01-11-2013,64.65
+01-12-2013,66.34
+01-01-2014,68.09
+01-02-2014,69.91
+01-03-2014,70.27
+01-04-2014,72.99
+01-05-2014,74.74
+01-06-2014,75.8
+01-07-2014,77.74
+01-08-2014,79.79
+01-09-2014,85.46
+01-10-2014,86.74
+01-11-2014,88.83
+01-12-2014,88.31
+01-01-2015,88.05
+01-02-2015,85.41
+01-03-2015,81.34
+01-04-2015,79.12
+01-05-2015,78.49
+01-06-2015,76.38
+01-07-2015,75.01
+01-08-2015,72.59
+01-09-2015,73.31
+01-10-2015,73.89
+01-11-2015,77.35
+01-12-2015,78.5
+01-01-2016,77.95
+01-02-2016,76.67
+01-03-2016,75.75
+01-04-2016,76.16
+01-05-2016,77.85
+01-06-2016,77.9
+01-07-2016,77.43
+01-08-2016,76.19
+01-09-2016,76.49
+01-10-2016,79.15
+01-11-2016,83.92
+01-12-2016,91.29
+01-01-2017,96.09
+01-02-2017,95.8
+01-03-2017,85.58
+01-04-2017,87.81
+01-05-2017,90.29
+01-06-2017,95.48
+01-07-2017,96.01
+01-08-2017,95.76
+01-09-2017,98.19
+01-10-2017,101.84
+01-11-2017,104.78
+01-12-2017,103.61
+01-01-2018,101.72
+01-02-2018,96.29
+01-03-2018,94.49
+01-04-2018,96.13
+01-05-2018,100.09
+01-06-2018,97.27
+01-07-2018,94.48
+01-08-2018,92.09
+01-09-2018,91.46
+01-10-2018,91.97
+01-11-2018,89.27
+01-12-2018,89.9
+01-01-2019,93
+01-02-2019,91.33
+01-03-2019,91.36
+01-04-2019,88.48
+01-05-2019,90.15
+01-06-2019,88.56
+01-07-2019,87.99
+01-08-2019,89.06
+01-09-2019,92.08
+01-10-2019,96.74
+01-11-2019,99.21
+01-12-2019,103.29
+01-01-2020,94.66
+01-02-2020,98
+01-03-2020,98
+01-05-2020,96
+01-06-2020,100.19
+01-07-2020,98.97
+01-08-2020,99.16
+01-09-2020,99.85
+01-01-2021,110.93
+01-02-2021,107.15
+01-03-2021,108.65
+01-04-2021,113.84
+01-05-2021,117.56
+01-06-2021,127.5
+01-07-2021,121.77
+01-08-2021,119.95
+01-09-2021,110.22
+01-10-2021,124.68
+01-11-2021,139.03
+01-12-2021,138.5
+01-01-2022,158.14
+01-02-2022,161.16
+01-03-2022,173.79
+01-04-2022,211.24
+01-05-2022,217.29
+01-06-2022,247.14
+01-07-2022,250.07
+01-08-2022,239.24
+01-09-2022,228.44
+01-10-2022,224.78
+01-11-2022,222.28
+01-12-2022,218.2

content/turmeric_price_6_months_with_dates.csv

@@ -0,0 +1,27 @@
+Date,Week,Price
+07-04-2024,Week 1,100
+14-04-2024,Week 2,105
+21-04-2024,Week 3,110
+28-04-2024,Week 4,115
+05-05-2024,Week 5,120
+12-05-2024,Week 6,115
+19-05-2024,Week 7,125
+26-05-2024,Week 8,130
+02-06-2024,Week 9,135
+09-06-2024,Week 10,130
+16-06-2024,Week 11,140
+23-06-2024,Week 12,145
+30-06-2024,Week 13,150
+07-07-2024,Week 14,145
+14-07-2024,Week 15,140
+21-07-2024,Week 16,135
+28-07-2024,Week 17,130
+04-08-2024,Week 18,125
+11-08-2024,Week 19,120
+18-08-2024,Week 20,115
+25-08-2024,Week 21,110
+01-09-2024,Week 22,105
+08-09-2024,Week 23,100
+15-09-2024,Week 24,110
+22-09-2024,Week 25,120
+29-09-2024,Week 26,130