Create app.py

app.py

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+import streamlit as st
+
+
+
+
+def god(STATE):
+    import requests
+    from bs4 import BeautifulSoup
+    import pandas as pd
+    import openpyxl
+    import os
+
+    output_directory = f"D:/{STATE}"
+
+    if not os.path.exists(output_directory):
+        os.makedirs(output_directory)
+    
+    def arbaj(STATE):
+        STATE_URL = f"https://www.censusindia2011.com/{STATE}-population.html"
+
+        def fetch_html(url):
+            try:
+                response = requests.get(url)
+                if response.status_code == 200:
+                    return response.text
+                else:
+                    print(f"Failed to fetch HTML. Status code: {response.status_code}")
+                    return None
+            except Exception as e:
+                print(f"An error occurred: {str(e)}")
+                return None
+
+        url = STATE_URL
+        html = fetch_html(url)
+
+        def extract_district_names(html):
+            district_names = []
+            soup = BeautifulSoup(html, 'html.parser')
+            tables = soup.find_all('table')
+            for table in tables:
+                headers = [header.text.strip() for header in table.find_all('th')]
+                if 'District' in headers:
+                    rows = table.find_all('tr')
+                    for row in rows[1:]:
+                        cells = row.find_all('td')
+                        district_name = cells[0].text.strip()
+                        district_names.append(district_name)
+            return district_names
+
+        district_names = extract_district_names(html)
+        return district_names
+
+    districts = arbaj(STATE)
+
+    def format_district_name(district_name):
+        if ' ' in district_name:
+            return district_name.replace(" ", "-")
+        else:
+            return district_name
+
+    for district in districts:
+        formatted_district_name = format_district_name(district)
+        DISTRICT_URL = f"https://www.censusindia2011.com/{STATE}/{formatted_district_name}-population.html"
+        def fetch_html(url):
+            try:
+                response = requests.get(url)
+                if response.status_code == 200:
+                    return response.text
+                else:
+                    print(f"Failed to fetch HTML. Status code: {response.status_code}")
+                    return None
+            except Exception as e:
+                print(f"An error occurred: {str(e)}")
+                return None
+        html = fetch_html(DISTRICT_URL)
+        wb = openpyxl.Workbook()
+
+        def extract_taluka_names(html):
+            taluka_names = []
+            soup = BeautifulSoup(html, 'html.parser')
+            tables = soup.find_all('table')
+
+            for table in tables:
+                headers = [header.text.strip() for header in table.find_all('th')]
+                taluka_headers = ['Taluka', 'Taluk', 'Mandal', 'Tehsil']  # Add more variations if needed
+                matching_headers = set(taluka_headers) & set(headers)
+
+                if matching_headers:
+                    rows = table.find_all('tr')
+                    for row in rows[1:]:
+                        cells = row.find_all('td')
+                        #taluka_name = cells[0].text.strip()
+                        taluka_name = cells[0].text.strip().replace(" ", "-")  # Replace spaces with hyphens
+                        taluka_names.append(taluka_name)
+                    break  # Break the loop if taluka names are found
+
+            return taluka_names
+
+        taluka_names = extract_taluka_names(html)
+
+        for taluka_name in taluka_names:
+            url = f"https://www.censusindia2011.com/{STATE}/{formatted_district_name}/{taluka_name}-population.html"
+            def get_html_inside_div(url, div_class):
+                # Fetch the webpage content
+                response = requests.get(url)
+
+                # Check if the request was successful
+                if response.status_code == 200:
+                    # Parse the HTML content
+                    soup = BeautifulSoup(response.content, 'html.parser')
+
+                    # Find the div with the specified class
+                    div = soup.find('div', class_=div_class)
+
+                    # Check if the div is found
+                    if div:
+                        # Return the HTML content inside the div
+                        return str(div)
+                    else:
+                        return "Div with class '{}' not found on the page.".format(div_class)
+                else:
+                    return "Failed to retrieve webpage. Status code: {}".format(response.status_code)
+            html_inside_div = get_html_inside_div(url, 'mt20')
+            html = str(html_inside_div)
+
+            def html_to_table(html):
+                soup = BeautifulSoup(html, 'html.parser')
+                all_tables = soup.find_all('table')
+                result = []
+                for i, table in enumerate(all_tables):
+                    headers = [header.text.strip() for header in table.find_all('th')]
+                    if all(col_name in headers for col_name in ['Village', 'Population', 'Literacy', 'Sex-ratio']) or all(col_name in headers for col_name in ['Town', 'Population', 'Literacy', 'Sex-ratio']):
+                        rows = []
+                        for row in table.find_all('tr'):
+                            cells = [cell.text.strip() for cell in row.find_all('td')]
+                            if cells:
+                                rows.append(cells)
+                        result.append((headers, rows))
+                return result
+
+            tables = html_to_table(html)
+
+            combined_sheet = wb.create_sheet(title=f'{taluka_name}')
+
+            for s, (headers, rows) in enumerate(tables):
+                df = pd.DataFrame(rows, columns=headers)
+
+                # Append the data to the combined sheet
+                for r_idx, row in enumerate(df.values, 1):
+                    for c_idx, value in enumerate(row, 1):
+                        combined_sheet.cell(row=r_idx, column=c_idx, value=value)
+
+            # Remove the default sheet created by openpyxl (Sheet)
+            if 'Sheet' in wb.sheetnames:
+                wb.remove(wb['Sheet'])
+
+            # Save the workbook
+        wb.save(os.path.join(output_directory, f'{district}.xlsx'))
+
+
+
+
+
+st.title("GENERICART")
+#st.write("If name of STATE contains more than 1 word then join with - ,like uttar-pradesh")
+state_names = [
+    'Uttar-Pradesh', 'Maharashtra', 'Bihar', 'West-Bengal', 'Andhra-Pradesh',
+    'Madhya-Pradesh', 'Tamil-Nadu', 'Rajasthan', 'Karnataka', 'Gujarat',
+    'Odisha', 'Kerala', 'Jharkhand', 'Assam', 'Punjab',
+    'Haryana', 'NCT-Of-Delhi', 'Jammu-&-Kashmir', 'Uttarakhand',
+    'Himachal-Pradesh', 'Tripura', 'Meghalaya', 'Manipur', 'Nagaland',
+    'Goa', 'Arunachal-Pradesh', 'Puducherry', 'Mizoram', 'Chandigarh',
+    'Sikkim', 'Andaman-&-Nicobar-Islands', 'Dadra-&-Nagar-Haveli',
+    'Daman-&-Diu', 'Lakshadweep']
+
+state=st.selectbox("Select a state:", state_names)
+
+# Button to trigger the processing function
+if st.button("Run Processing"):
+    # Show a spinner while the processing is ongoing
+    with st.spinner("Processing..."):
+        # Call your time-consuming function here
+        god(state)
+
+    # Once the processing is done, remove the spinner
+    st.success("Processing complete!")
+
+st.subheader(f"Data Organization in Folder")
+
+# Write information about the Excel file and data organization
+st.write(
+    f"In the D drive, folder has been identified, named after the specified state. "
+    f"This file encompasses multiple sheets, each dedicated to individual districts within the state. "
+    f"The organization of data within these sheets adheres to a structured format, where the sheet names correspond to the respective talukas."
+)
+
+# Display information about the columns in the DataFrame
+st.write("Each sheet contains essential demographic information, meticulously arranged in columns for ease of analysis. The columns include:")
+st.write("- **VILLAGE:** This field provides a comprehensive list of villages within the taluka, offering a granular view of the geographic distribution.")
+st.write("- **POPULATION:** The population column quantifies the total number of residents in each village, facilitating an understanding of settlement sizes.")
+st.write("- **LITERACY:** Literacy rates are documented, reflecting the proportion of individuals who possess basic reading and writing skills in the specified villages.")
+st.write("- **SEX RATIO:** The sex ratio column provides insights into the gender distribution within the villages, aiding in the evaluation of gender demographics.")