Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# Load the data files
+cleaned_data = pd.read_csv('cleaned_data.csv')
+veg_index_data = pd.read_csv('SentinelTimeSeries_Sugarcane (2) (3).csv')
+weather_data = pd.read_csv('weather_data.csv')  # Replace with your weather data file
+performance_estimation_data = pd.read_csv('تخمین تناژ.csv')  # Replace with your yield estimation file
+
+# Function to search for farm performance
+def search_farm(farm_name):
+    # Filter the farm's data for recent years (1392-1402)
+    farm_data = cleaned_data[cleaned_data['مزرعه'] == farm_name]
+    farm_performance = farm_data[(farm_data['year'] >= 1392) & (farm_data['year'] <= 1402)]
+    
+    # Create a time-series chart for the vegetation index (mean_VH, mean_VV)
+    veg_data = veg_index_data[veg_index_data['مزرعه'] == farm_name]
+    
+    # Plotting vegetation index trends
+    fig, ax = plt.subplots(figsize=(10,5))
+    ax.plot(veg_data['date'], veg_data['mean_VH'], label='mean_VH')
+    ax.plot(veg_data['date'], veg_data['mean_VV'], label='mean_VV')
+    ax.set_xlabel('Date')
+    ax.set_ylabel('Vegetation Index')
+    ax.set_title(f'Vegetation Index Trends for {farm_name}')
+    ax.legend()
+    plt.xticks(rotation=45)
+    
+    return farm_performance, fig
+
+# Function to display weather data
+def show_weather_data():
+    st.dataframe(weather_data)
+
+# Function to predict farm performance (yield and tonnage)
+def predict_performance(farm_name):
+    prediction_data = performance_estimation_data[performance_estimation_data['مزرعه'] == farm_name]
+    
+    if prediction_data.empty:
+        st.write("مزرعه مورد نظر پیدا نشد.")
+    else:
+        yield_value = prediction_data['yield'].values[0]
+        tonnage = prediction_data['tonnage'].values[0]  # Adjust based on your actual column name
+        st.write(f"تن در هکتار: {yield_value}")
+        st.write(f"تناژ: {tonnage}")
+
+# Streamlit app
+st.title("Farm Performance Dashboard")
+
+# Sidebar menu
+menu = st.sidebar.selectbox("انتخاب منو", ["Farm Search", "دیتابیس هواشناسی", "پیش بینی عملکرد"])
+
+# Farm Search section
+if menu == "Farm Search":
+    st.header("Search for Farm Performance")
+    farm_name = st.text_input("نام مزرعه را وارد کنید")
+    
+    if st.button("Search"):
+        farm_performance, veg_chart = search_farm(farm_name)
+        st.write("Farm Performance (1392-1402):")
+        st.dataframe(farm_performance)
+        st.write("Vegetation Index Time Series:")
+        st.pyplot(veg_chart)
+
+# Weather Database section
+elif menu == "دیتابیس هواشناسی":
+    st.header("Weather Data")
+    st.write("مشاهده داده‌های هواشناسی:")
+    show_weather_data()
+
+# Performance Prediction section
+elif menu == "پیش بینی عملکرد":
+    st.header("Predict Farm Performance")
+    farm_name_prediction = st.text_input("نام مزرعه برای پیش بینی")
+    
+    if st.button("پیش بینی"):
+        predict_performance(farm_name_prediction)