app.py

import streamlit as st
import pandas as pd
import requests
import base64

def get_backlinks(api_login, api_key, target_url, filters):
    # Encoding credentials
    encoded_credentials = base64.b64encode(f"{api_login}:{api_key}".encode()).decode()

    # Setting headers with Basic Authentication
    headers = {
        'Authorization': f'Basic {encoded_credentials}'
    }

    # Prepare post data
    post_data = {
        0: {
            "target": target_url,
            "limit": 1000,
            "mode": "as_is",
            "filters": filters
        }
    }

    # Making the API request
    response = requests.post("https://api.dataforseo.com/v3/backlinks/backlinks/live", json=post_data, headers=headers)

    # Log the full response for debugging
    st.text("API Response:")
    # st.json(response.json())  # This will display the full JSON response in the Streamlit app

    # For debugging: Display the status code and any text
    st.text(f"Response Status Code: {response.status_code}")
    st.text(f"Response Headers: {response.headers}")
    try:
        st.text(f"Response Body: {response.json()}")
    except ValueError:
        st.text("Response Body: <Not a JSON response>")

    # Check if the response contains 'results' key
    if response.status_code == 200:
        response_data = response.json()
        
        # Check if 'results' key is in the response
        if 'results' in response_data:
            results = response_data['results']
            if results:
                # Adjust the following line based on the actual JSON structure
                df = pd.json_normalize(results)
                return df
            else:
                st.error("Received empty data from API.")
                return None
        else:
            # Handle other internal status codes here
            internal_status_code = response_data.get('status_code', None)
            internal_status_message = response_data.get('status_message', 'No specific message provided')
            st.error(f"Internal Status Code: {internal_status_code}, Message: {internal_status_message}")
            return None
    else:
        error_message = response.json().get('status_message', 'No specific error message provided')
        st.error(f"Error: Code: {response.status_code} Message: {error_message}")
        return None

def convert_df_to_csv(df):
    # Convert DataFrame to CSV
    return df.to_csv().encode('utf-8')

# Streamlit layout
st.sidebar.title("DataForSEO API Parameters")
api_login = st.sidebar.text_input("API Login", value="[email protected]")
api_key = st.sidebar.text_input("API Key", type="password")

# Filters input
url_from_not_contain = st.sidebar.text_input("URL from does not contain (comma-separated)")
is_lost = st.sidebar.checkbox("Is Lost", value=False)
dofollow = st.sidebar.checkbox("Dofollow", value=True)
backlink_spam_score = st.sidebar.slider("Backlink Spam Score ≤", 0, 100, 10)
page_from_language = st.sidebar.selectbox("Page From Language", ['en', 'other'])

# Prepare filters for API call
filters = []

if url_from_not_contain:
    for url in url_from_not_contain.split(','):
        filters.append(["url_from", "not_like", url.strip()])
        filters.append("and")

if is_lost:
    filters.append(["is_lost", "=", is_lost])
    filters.append("and")

if dofollow:
    filters.append(["dofollow", "=", dofollow])
    filters.append("and")

filters.append(["backlink_spam_score", "<=", backlink_spam_score])
filters.append("and")
filters.append(["page_from_language", "=", page_from_language])

# Remove the last "and" if it's the last element
if filters and filters[-1] == "and":
    filters.pop()

# Main app layout
col1, col2 = st.columns(2)

with col1:
    st.header("Input")
    target_url = st.text_input("Enter the target URL")  # Define target_url here

generate_button = st.sidebar.button("Generate All")
reset_button = st.sidebar.button("Reset")

# Generate CSV and download button
if generate_button and target_url:
    df = get_backlinks(api_login, api_key, target_url, filters)
    if df is not None:
        # Convert DataFrame to CSV
        csv = convert_df_to_csv(df)
        # Create download link
        st.download_button(
            label="Download data as CSV",
            data=csv,
            file_name='backlinks.csv',
            mime='text/csv',
        )

# Reset functionality
if reset_button:
    st.experimental_rerun()