Mortgage rate capture

from playwright.sync_api import sync_playwright
from browserbase import Browserbase
import os
import base64
from dotenv import load_dotenv

load_dotenv()


def capture_mortgage_rates():
    # Create BrowserBase session and take screenshot
    bb = Browserbase(api_key=os.environ["BROWSERBASE_API_KEY"])
    session = bb.sessions.create()

    with sync_playwright() as playwright:
        # Setup browser
        browser = playwright.chromium.connect_over_cdp(session.connect_url)
        context = browser.new_context()
        page = context.new_page()

        # Navigate to Freddie Mac's mortgage rates page
        page.goto("https://www.freddiemac.com/pmms")

        # Navigate and screenshot
        page.locator("#main-content iframe").content_frame.get_by_role(
            "heading", name="Primary Mortgage Market"
        ).scroll_into_view_if_needed()

        # Take a screenshot via CDP for better performance
        client = context.new_cdp_session(page)
        screenshot_data = client.send(
            "Page.captureScreenshot",
            {
                "format": "jpeg",
                "quality": 80,
                "fullpage": True,
            },
        )

        # Save the screenshot
        image_data = base64.b64decode(screenshot_data["data"])
        with open("freddie_mac_rates.jpeg", "wb") as f:
            f.write(image_data)

        # Clean up
        browser.close()

    return "freddie_mac_rates.jpeg"


if __name__ == "__main__":
    screenshot_path = capture_mortgage_rates()
    print(f"Screenshot saved as '{screenshot_path}'")

from ibm_watsonx_ai import APIClient, Credentials
from ibm_watsonx_ai.foundation_models import ModelInference
import base64
import os
import datetime
from dotenv import load_dotenv

load_dotenv()


def get_image_base64(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode("utf-8")


def analyze_mortgage_rates(image_path):
    # Get base64 encoded image
    image_base64 = get_image_base64(image_path)

    # Set up IBM watsonx.ai
    credentials = Credentials(
        # use your IBM URL based on region
        url="https://us-south.ml.cloud.ibm.com",
        api_key=os.getenv("IBM_API_KEY"),
    )

    client = APIClient(credentials)
    project_id = os.getenv("IBM_PROJECT_ID")

    print(f"Using IBM watsonx.ai project ID: {project_id}")

    # Initialize the Llama 3 vision model
    model = ModelInference(
        model_id="meta-llama/llama-3-2-90b-vision-instruct",
        api_client=client,
        project_id=project_id,
        params={
            "max_new_tokens": 1000,
            "time_limit": 10000,
        },
    )

    # Prepare prompt with the image
    messages = [
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:image/jpeg;base64,{image_base64}",
                    },
                },
                {
                    "type": "text",
                    "text": "Please provide a clear summary of the current mortgage rates shown in this Freddie Mac PMMS page. Format the response as a brief market update, including the date and the rates for 30-year and 15-year fixed mortgages.",
                },
            ],
        },
    ]

    # Generate the analysis
    print("Analyzing mortgage rates with IBM watsonx.ai...")
    response = model.chat(
        messages=messages,
        params={
            "input_type": "chat",
        },
    )

    # Extract content from response
    if isinstance(response, dict):
        content = (
            response.get(
                "choices",
                [
                    {},
                ],
            )[0]
            .get("message", {})
            .get("content", "")
        )
    else:
        # Some API versions return the content directly
        content = response

    return content


if __name__ == "__main__":
    analysis = analyze_mortgage_rates("freddie_mac_rates.jpeg")

from screenshot import capture_mortgage_rates
from analyze import analyze_mortgage_rates


def main():
    # Capture the screenshot
    print("Step 1: Capturing screenshot of mortgage rates...")
    screenshot_path = capture_mortgage_rates()

    # Analyze the rates
    print("\nStep 2: Analyzing image...")
    analysis = analyze_mortgage_rates(screenshot_path)

    # Display the results
    print("\nMortgage Rate Analysis:")
    print(analysis)


if __name__ == "__main__":
    main()