ea5554a723
Code Improvements: Error Handling: Improve error messages to be more informative. Log errors for debugging purposes. Code Readability: Add docstrings and comments to explain the purpose of functions and complex code blocks. Modularization: Split large functions into smaller, reusable functions. Group related functions together. Input Validation: Ensure user inputs are validated to prevent errors later in the code. User Experience Improvements: User Feedback: Provide immediate feedback on actions (e.g., loading spinners, success, and error messages). Use placeholders and help text to guide users on what inputs are expected. Interactive Elements: Use more interactive elements like sliders, date pickers, and multi-selects to enhance the user interface.
131 lines
4.8 KiB
Python
131 lines
4.8 KiB
Python
import streamlit as st
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import json
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from datetime import date
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from dotenv import load_dotenv
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from ..ai_web_researcher.firecrawl_web_crawler import scrape_url
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from ..gpt_providers.text_generation.main_text_generation import llm_text_gen
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# Load environment variables
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load_dotenv()
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# Define a dictionary for schema types
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schema_types = {
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"Article": {
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"fields": ["Headline", "Author", "Date Published", "Keywords"],
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"schema_type": "Article",
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},
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"Product": {
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"fields": ["Name", "Description", "Price", "Brand", "Image URL"],
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"schema_type": "Product",
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},
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"Recipe": {
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"fields": ["Name", "Ingredients", "Cooking Time", "Serving Size", "Image URL"],
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"schema_type": "Recipe",
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},
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"Event": {
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"fields": ["Name", "Start Date", "End Date", "Location", "Description"],
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"schema_type": "Event",
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},
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"LocalBusiness": {
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"fields": ["Name", "Address", "Phone Number", "Opening Hours", "Image URL"],
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"schema_type": "LocalBusiness",
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},
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# ... (add more schema types as needed)
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}
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def generate_json_data(content_type, details, url):
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"""Generates structured data (JSON-LD) based on user input."""
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try:
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scraped_text = scrape_url(url)
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except Exception as err:
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st.error(f"Failed to scrape web page from URL: {url} - Error: {err}")
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return
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schema = schema_types.get(content_type)
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if not schema:
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st.error(f"Invalid content type: {content_type}")
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return
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data = {
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"@context": "https://schema.org",
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"@type": schema["schema_type"],
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}
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for field in schema["fields"]:
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value = details.get(field)
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if isinstance(value, date):
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value = value.isoformat()
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data[field] = value if value else "N/A" # Use placeholder values if input is missing
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if url:
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data['url'] = url
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llm_structured_data = get_llm_structured_data(content_type, data, scraped_text)
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return llm_structured_data
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def get_llm_structured_data(content_type, data, scraped_text):
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"""Function to get structured data from LLM."""
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prompt = f"""Given the following information:
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HTML Content: <<<HTML>>> {scraped_text} <<<END_HTML>>>
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Content Type: <<<CONTENT_TYPE>>> {content_type} <<<END_CONTENT_TYPE>>>
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Additional Relevant Data: <<<ADDITIONAL_DATA>>> {data} <<<END_ADDITIONAL_DATA>>>
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Create a detailed structured data (JSON-LD) script for SEO purposes.
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The structured data should help search engines understand the content and features of the webpage, enhancing its visibility and potential for rich snippets in search results.
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Detailed Steps:
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Parse the HTML content to extract relevant information like the title, main heading, and body content.
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Use the contentType to determine the structured data type (e.g., Article, Product, Recipe).
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Integrate the additional relevant data (e.g., author, datePublished, keywords) into the structured data.
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Ensure all URLs, images, and other attributes are correctly formatted and included.
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Validate the generated JSON-LD to ensure it meets schema.org standards and is free of errors.
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Expected Output:
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Generate a JSON-LD structured data snippet based on the provided inputs."""
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try:
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response = llm_text_gen(prompt)
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return response
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except Exception as err:
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st.error(f"Failed to get response from LLM: {err}")
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return
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def ai_structured_data():
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st.title("📝 Generate Structured Data for SEO 🚀")
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st.markdown("**Make your content more discoverable with rich snippets.**")
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content_type = st.selectbox("**Select Content Type**", list(schema_types.keys()))
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details = {}
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schema_fields = schema_types[content_type]["fields"]
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num_fields = len(schema_fields)
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url = st.text_input("**URL :**", placeholder="Enter the URL of your webpage")
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for i in range(0, num_fields, 2):
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cols = st.columns(2)
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for j in range(2):
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if i + j < num_fields:
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field = schema_fields[i + j]
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if "Date" in field:
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details[field] = cols[j].date_input(field)
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else:
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details[field] = cols[j].text_input(field, placeholder=f"Enter {field.lower()}")
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if st.button("Generate Structured Data"):
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if not url:
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st.error("URL is required to generate structured data.")
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return
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structured_data = generate_json_data(content_type, details, url)
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if structured_data:
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st.subheader("Generated Structured Data (JSON-LD):")
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st.markdown(structured_data)
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st.download_button(
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label="Download JSON-LD",
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data=structured_data,
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file_name=f"{content_type}_structured_data.json",
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mime="application/json",
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)
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