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| import gradio as gr | |
| import requests | |
| from bs4 import BeautifulSoup | |
| from nltk import download, sent_tokenize | |
| import google.generativeai as genai | |
| import os | |
| import re | |
| import tempfile | |
| import asyncio | |
| # Download NLTK data | |
| download('punkt') | |
| download('punkt_tab') | |
| # Configure Gemini API using environment variable | |
| api_key = os.environ.get("GEMINI_API_KEY") | |
| if not api_key: | |
| raise ValueError("GEMINI_API_KEY not found in environment variables. Please set it in your environment.") | |
| genai.configure(api_key=api_key) | |
| # Use gemini-1.5-flash for faster text analysis | |
| try: | |
| model = genai.GenerativeModel('gemini-1.5-flash') | |
| except Exception as e: | |
| print(f"Error initializing model: {str(e)}") | |
| print("Available models:") | |
| for m in genai.list_models(): | |
| print(m.name) | |
| raise ValueError("Failed to initialize gemini-1.5-flash. Check available models above and update the model name.") | |
| # Prompt for Gemini to analyze text | |
| PROMPT = """ | |
| You are an AI content reviewer. Analyze the provided text for the following: | |
| 1. *Grammar Issues*: Identify and suggest corrections for grammatical errors. | |
| 2. *Legal Policy Violations*: Flag content that may violate common legal policies (e.g., copyright infringement, defamation, incitement to violence). | |
| 3. *Crude/Abusive Language*: Detect crude, offensive, or abusive language. | |
| 4. *Sensitive Topics*: Identify content related to sensitive topics such as racism, gender bias, or other forms of discrimination. | |
| Return the results in the following markdown format: | |
| # Blog Review Report | |
| ## Grammar Corrections | |
| 1. [Heading of issue] | |
| - CONTENT: [Exact line or part of text with the issue] | |
| - SUGGESTION: [Suggested correction] | |
| - ISSUE: [Description of the issue] | |
| 2. [Heading of next issue] | |
| - CONTENT: [Exact line or part of text with the issue] | |
| - SUGGESTION: [Suggested correction] | |
| - ISSUE: [Description of the issue] | |
| [Continue numbering for additional issues or state "None detected"] | |
| ## Legal Policy Violations | |
| - CONTENT: [Exact line or part of text with the issue] | |
| SUGGESTION: [Suggested action or correction] | |
| ISSUE: [Description of the legal violation] | |
| [Or state "None detected"] | |
| ## Crude/Abusive Language | |
| - [List instances of crude or abusive language or "None detected"] | |
| ## Sensitive Topics | |
| - [List instances of sensitive topics or "None detected"] | |
| For each issue, provide the exact text, a suggested correction or action, and a concise explanation. Be precise and ensure the output strictly follows the specified format. | |
| """ | |
| async def fetch_url_content(url): | |
| try: | |
| response = requests.get(url, timeout=10) | |
| response.raise_for_status() | |
| soup = BeautifulSoup(response.text, 'html.parser') | |
| content = ' '.join([p.get_text(strip=True) for p in soup.find_all(['p', 'article', 'div'])]) | |
| return content if content else "No readable content found on the page." | |
| except Exception as e: | |
| return f"Error fetching URL: {str(e)}" | |
| async def review_blog(text_input, url_input): | |
| # Initialize output variables | |
| button_text = "Processing..." | |
| report = "" | |
| download_path = None | |
| # Determine input type | |
| if text_input and not url_input: | |
| input_type = "Text" | |
| input_text = text_input | |
| elif url_input and not text_input: | |
| input_type = "URL" | |
| input_text = url_input | |
| else: | |
| return "Review Blog", "Error: Please provide input in either the Text or URL tab, but not both.", gr.update(visible=False) | |
| # Handle empty input | |
| if not input_text: | |
| return "Review Blog", "Error: No input provided.", gr.update(visible=False) | |
| try: | |
| # Wrap the entire process in a timeout | |
| async def process_with_timeout(): | |
| nonlocal button_text, report, download_path | |
| # Handle URL input | |
| if input_type == "URL": | |
| button_text = "Fetching content..." | |
| content = await fetch_url_content(input_text) | |
| if content.startswith("Error"): | |
| return "Review Blog", content, gr.update(visible=False) | |
| input_text_content = content | |
| else: | |
| input_text_content = input_text | |
| # Tokenize input for analysis | |
| sentences = sent_tokenize(input_text_content) | |
| analysis_text = "\n".join(sentences) | |
| # Update button for API call | |
| button_text = "Generating report..." | |
| try: | |
| response = await asyncio.to_thread(model.generate_content, PROMPT + "\n\nText to analyze:\n" + analysis_text) | |
| report = response.text.strip() | |
| report = re.sub(r'^```markdown\n|```$', '', report, flags=re.MULTILINE) | |
| except Exception as e: | |
| report = f"Error analyzing content with Gemini: {str(e)}. Please check your API key, network connection, or model availability." | |
| print("Available models:") | |
| for m in genai.list_models(): | |
| print(m.name) | |
| return "Review Blog", report, gr.update(visible=False) | |
| # Create a temporary file to store the report | |
| try: | |
| with tempfile.NamedTemporaryFile(mode='w', suffix='.md', delete=False, encoding='utf-8') as temp_file: | |
| temp_file.write(report) | |
| download_path = temp_file.name | |
| report = f"**Report generated, please scroll down to view.**\n\n{report}" | |
| return "Review Blog", report, gr.update(visible=True, value=download_path) | |
| except Exception as e: | |
| return "Review Blog", f"Error creating temporary file: {str(e)}", gr.update(visible=False) | |
| # Execute with timeout | |
| return await asyncio.wait_for(process_with_timeout(), timeout=30) | |
| except asyncio.TimeoutError: | |
| return "Review Blog", "Error: Process timed out after 30 seconds.", gr.update(visible=False) | |
| except Exception as e: | |
| return "Review Blog", f"Unexpected error: {str(e)}", gr.update(visible=False) | |
| # Custom CSS for hover effect, loading state, and Inter font | |
| custom_css = """ | |
| @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap'); | |
| .gradio-container { | |
| font-family: 'Inter', sans-serif !important; | |
| } | |
| .review-btn { | |
| transition: all 0.3s ease; | |
| font-weight: 500; | |
| background-color: #2c3e50; | |
| color: white; | |
| border-radius: 8px; | |
| padding: 10px 20px; | |
| position: relative; | |
| } | |
| .review-btn:hover { | |
| background-color: #4CAF50; | |
| color: white; | |
| transform: scale(1.05); | |
| } | |
| .review-btn:disabled { | |
| opacity: 0.7; | |
| cursor: not-allowed; | |
| } | |
| .review-btn:disabled::before { | |
| content: ''; | |
| display: inline-block; | |
| width: 16px; | |
| height: 16px; | |
| border: 2px solid #fff; | |
| border-radius: 50%; | |
| border-top-color: transparent; | |
| animation: spin 1s linear infinite; | |
| margin-right: 8px; | |
| vertical-align: middle; | |
| } | |
| @keyframes spin { | |
| 0% { transform: rotate(0deg); } | |
| 100% { transform: rotate(360deg); } | |
| } | |
| .tab-nav button { | |
| font-family: 'Inter', sans-serif; | |
| font-weight: 500; | |
| } | |
| input, textarea { | |
| font-family: 'Inter', sans-serif; | |
| } | |
| """ | |
| # Gradio UI with Tabs | |
| with gr.Blocks(theme=gr.themes.Monochrome(), css=custom_css) as demo: | |
| gr.Markdown("# 📝 AI Blog Reviewer") | |
| gr.Markdown("Enter blog text or a URL to review for grammar, legal issues, crude language, and sensitive topics. The report is generated in markdown format.") | |
| with gr.Tabs(): | |
| with gr.TabItem("Text"): | |
| text_input = gr.Textbox(lines=8, label="Blog Content", placeholder="Paste your blog text here...") | |
| with gr.TabItem("URL"): | |
| url_input = gr.Textbox(lines=1, label="Blog URL", placeholder="Enter the blog URL here...") | |
| status_button = gr.Button(value="Review Blog", elem_classes=["review-btn"]) | |
| gr.Markdown("### 📄 Review Report") | |
| report_output = gr.Markdown() | |
| download_btn = gr.File(label="Download Report", visible=False) | |
| # Bind the review button to process inputs | |
| status_button.click( | |
| fn=review_blog, | |
| inputs=[text_input, url_input], | |
| outputs=[status_button, report_output, download_btn] | |
| ) | |
| demo.launch() |