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import gradio as gr
import asyncio
import subprocess
import sys
import os
from datetime import datetime
os.environ.setdefault("PLAYWRIGHT_BROWSERS_PATH", "/home/user/.cache/ms-playwright")
def install_playwright_browsers():
    marker = os.path.join(os.environ["PLAYWRIGHT_BROWSERS_PATH"], ".installed")
    if os.path.exists(marker):
        print("✅ Playwright browsers already installed, skipping.")
        return
    try:
        print("📦 Installing Playwright browsers...")
        subprocess.check_call(
            [sys.executable, "-m", "playwright", "install", "chromium"],
        )
        os.makedirs(os.environ["PLAYWRIGHT_BROWSERS_PATH"], exist_ok=True)
        open(marker, "w").close()
        print("✅ Playwright browsers installed successfully.")
    except subprocess.CalledProcessError as e:
        print(f"❌ Playwright install failed with code {e.returncode}")
    except Exception as e:
        print(f"❌ Unexpected error during install: {e}")

install_playwright_browsers()

try:
    import spaces
    HAS_SPACES = True
except ImportError:
    HAS_SPACES = False

if HAS_SPACES:
    @spaces.GPU
    def dummy_gpu():
        pass

from seo_analyzer import run_seo_analysis_fastapi
from ai_visibility import run_ai_visibility_analysis

# ---- Helper functions to format output ----

def format_seo_result(result):
    if isinstance(result, tuple):
        data, csv_path = result
        if not data:
            return "No data returned."
        output = f"## SEO Analysis Results\n"
        output += f"**Pages analyzed:** {len(data)}\n"
        scores = [p.get('seo_score', 0) for p in data]
        avg = sum(scores) / len(scores) if scores else 0
        output += f"**Average SEO Score:** {avg:.1f}/100\n\n"

        # Generate detailed strengths and issues based on page metrics
        detailed_strengths = []
        detailed_issues = []
        for page in data:
            url = page.get('url', '')
            # Title
            title_len = len(page.get('title', ''))
            if 50 <= title_len <= 60:
                detailed_strengths.append(f"- Strong title length ({title_len} chars) on {url}")
            elif title_len < 30 or title_len > 70:
                detailed_issues.append(f"- Title too {'short' if title_len < 30 else 'long'} ({title_len} chars) on {url}")
            # Meta description
            meta_len = len(page.get('meta_description', ''))
            if 120 <= meta_len <= 160:
                detailed_strengths.append(f"- Good meta description length ({meta_len} chars) on {url}")
            elif meta_len > 0 and (meta_len < 70 or meta_len > 170):
                detailed_issues.append(f"- Meta description length ({meta_len} chars) suboptimal on {url}")
            # H1
            h1 = page.get('h1_count', 0)
            if h1 == 1:
                detailed_strengths.append(f"- Exactly one H1 on {url}")
            elif h1 == 0:
                detailed_issues.append(f"- Missing H1 on {url}")
            elif h1 > 1:
                detailed_issues.append(f"- Multiple H1s ({h1}) on {url}")
            # Word count
            wc = page.get('word_count', 0)
            if wc >= 800:
                detailed_strengths.append(f"- Good word count ({wc}) on {url}")
            elif wc < 300:
                detailed_issues.append(f"- Low word count ({wc}) on {url}")
            # Alt tags
            total_img = page.get('total_images', 0)
            missing_alt = page.get('missing_alt_tags', 0)
            if total_img > 0 and missing_alt == 0:
                detailed_strengths.append(f"- All images have alt text on {url}")
            elif total_img > 0 and missing_alt > 0:
                detailed_issues.append(f"- {missing_alt} images missing alt text on {url}")
            # Schema
            schema = page.get('schema_types', '')
            if schema and schema != "No schema found":
                detailed_strengths.append(f"- Schema detected ({schema}) on {url}")
            else:
                detailed_issues.append(f"- No schema found on {url}")
            # Readability
            readability = page.get('readability_score', 0)
            if readability >= 50:
                detailed_strengths.append(f"- Good readability score ({readability}) on {url}")
            elif readability < 30:
                detailed_issues.append(f"- Poor readability ({readability}) on {url}")

        if detailed_strengths:
            output += "### SEO Strengths (detailed)\n"
            output += "\n".join(detailed_strengths) + "\n\n"
        if detailed_issues:
            output += "### SEO Issues (detailed)\n"
            output += "\n".join(detailed_issues) + "\n\n"

        # Per-page data
        for i, page in enumerate(data, 1):
            output += f"### Page {i}: {page.get('url', '')}\n"
            output += f"- Score: {page.get('seo_score', 0)}/100\n"
            output += f"- Title: {page.get('title', 'No title')}\n"
            output += f"- Word Count: {page.get('word_count', 0)}\n"
            output += f"- H1: {page.get('h1_count', 0)}, H2: {page.get('h2_count', 0)}, H3: {page.get('h3_count', 0)}\n"
            output += f"- Images: {page.get('total_images', 0)} (missing alt: {page.get('missing_alt_tags', 0)})\n"
            output += f"- Internal/External links: {page.get('internal_links', 0)}/{page.get('external_links', 0)}\n"
            output += f"- Readability: {page.get('readability_score', 0)}\n"
            output += f"- Grammar Errors: {page.get('grammar_errors', 0)}\n"
            output += f"- Canonical Tag: {'Yes' if page.get('canonical_tag') else 'No'}\n"
            output += f"- OpenGraph Tags: {page.get('opengraph_tags', 0)}\n"
            output += f"- Twitter Cards: {page.get('twitter_tags', 0)}\n"
            output += f"- Robots Meta: {page.get('robots_meta', 'none')}\n"
            output += f"- Viewport: {'Yes' if page.get('viewport_present') else 'No'}\n"
            output += f"- Schema Types: {page.get('schema_types', 'none')}\n"
            output += f"- Text/HTML Ratio: {page.get('text_to_html_ratio', 0)}%\n"
            output += f"- Load Time: {page.get('load_time', 0)}s\n"
            output += f"- Meta Description: {page.get('meta_description', '')}\n"
            output += f"- Heading Order: {page.get('heading_order', '')}\n"
            if page.get('ai_suggestions'):
                output += f"- AI Suggestions: {page['ai_suggestions'][:200]}...\n"
            output += "\n"
        return output
    else:
        return f"❌ Error: {result.get('message', 'Unknown error')}"

def format_ai_result(result):
    if result.get('status') == 'error':
        return f"❌ Error: {result.get('message', 'Unknown error')}"
    output = f"## AI Visibility / Readiness Analysis\n"
    output += f"**URL:** {result.get('url', '')}\n"
    output += f"**Pages analyzed:** {result.get('pages_analyzed', 0)}\n"
    output += f"**Overall AI Readiness Score:** {result.get('ai_readiness_score', 0)}/100\n"
    output += f"**Page types detected:** {result.get('page_type_breakdown', {})}\n\n"
    cat_scores = result.get('category_scores', {})
    if cat_scores:
        output += "### Category Scores\n"
        for k, v in cat_scores.items():
            output += f"- {k.replace('_score', '').replace('_', ' ').title()}: {v if v is not None else 'N/A'}\n"
        output += "\n"
    previews = result.get('results_preview', [])
    if previews:
        output += "### Per-Page Details\n"
        for p in previews:
            output += f"**URL:** {p.get('url', '')}\n"
            output += f"- Page Type: {p.get('page_type', 'unknown')} (conf: {p.get('page_type_confidence', 0):.2f})\n"
            output += f"- Readiness Score: {p.get('ai_readiness_score', 0)}/100\n"
            output += f"- Topic Clarity: {p.get('topic_clarity', 0)}\n"
            output += f"- Content Completeness: {p.get('content_completeness', 0)}\n"
            output += f"- Entity Clarity: {p.get('entity_clarity', 'N/A')}\n"
            output += f"- Freshness: {p.get('freshness_status', 'unknown')}\n\n"

    # Detailed issues and strengths (per page) from backend
    issues = result.get('issues', [])
    strengths = result.get('strengths', [])
    if issues:
        output += "### Detailed Issues (per page)\n"
        for issue in issues:
            output += f"- {issue.get('title')} (Severity: {issue.get('severity')}) on {issue.get('page')}\n"
            output += f"  Explanation: {issue.get('explanation')}\n"
            if issue.get('recommended_fix'):
                output += f"  Fix: {issue.get('recommended_fix')}\n"
    if strengths:
        output += "### Detailed Strengths (per page)\n"
        for strength in strengths:
            output += f"- {strength.get('title')} on {strength.get('page')}\n"
            output += f"  Detail: {strength.get('detail')}\n"

    return output

# ---- Async analysis wrappers ----
async def analyze_seo_async(url, max_pages, max_concurrent, use_ai):
    result = await run_seo_analysis_fastapi(
        base_url=url,
        max_pages=int(max_pages),
        use_ai=use_ai,
        max_concurrent=int(max_concurrent)
    )
    return format_seo_result(result)

async def analyze_ai_async(url, max_pages, max_concurrent, use_ai):
    result = await run_ai_visibility_analysis(
        base_url=url,
        max_pages=int(max_pages),
        max_concurrent=int(max_concurrent),
        use_ai=use_ai
    )
    return format_ai_result(result)

# ---- Gradio Interface ----
with gr.Blocks(title="SEO & AI Visibility Analyzer") as demo:
    gr.Markdown("# 🚀 SEO & AI Visibility Analysis Tool")
    gr.Markdown("Enter a website URL to analyze its SEO health and AI search readiness.")
    
    with gr.Row():
        with gr.Column(scale=2):
            url_input = gr.Textbox(label="Website URL", placeholder="https://example.com", value="https://example.com")
        with gr.Column(scale=1):
            max_pages_input = gr.Number(label="Max Pages", value=3, minimum=1, maximum=20, step=1)
        with gr.Column(scale=1):
            max_concurrent_input = gr.Number(label="Concurrent Browsers", value=1, minimum=1, maximum=5, step=1)
        with gr.Column(scale=1):
            use_ai_check = gr.Checkbox(label="Enable AI Suggestions", value=True)
    
    with gr.Row():
        seo_btn = gr.Button("🔍 Analyze SEO", variant="primary")
        ai_btn = gr.Button("🤖 Analyze AI Visibility", variant="secondary")
    
    output = gr.Markdown(label="Results")
    
    seo_btn.click(
        fn=analyze_seo_async,
        inputs=[url_input, max_pages_input, max_concurrent_input, use_ai_check],
        outputs=output
    )
    ai_btn.click(
        fn=analyze_ai_async,
        inputs=[url_input, max_pages_input, max_concurrent_input, use_ai_check],
        outputs=output
    )

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)












# import gradio as gr
# import asyncio
# import subprocess
# import sys
# import os
# from datetime import datetime
# os.environ.setdefault("PLAYWRIGHT_BROWSERS_PATH", "/home/user/.cache/ms-playwright")
# def install_playwright_browsers():
#     """Install Playwright Chromium browsers – works on Hugging Face Spaces."""
#     marker = os.path.join(os.environ["PLAYWRIGHT_BROWSERS_PATH"], ".installed")
#     if os.path.exists(marker):
#         print("✅ Playwright browsers already installed, skipping.")
#         return
#     try:
#         print("📦 Installing Playwright browsers...")
#         subprocess.check_call(
#             [sys.executable, "-m", "playwright", "install", "chromium"],
#         )
#         os.makedirs(os.environ["PLAYWRIGHT_BROWSERS_PATH"], exist_ok=True)
#         open(marker, "w").close()
#         print("✅ Playwright browsers installed successfully.")
#     except subprocess.CalledProcessError as e:
#         print(f"❌ Playwright install failed with code {e.returncode}")
#     except Exception as e:
#         print(f"❌ Unexpected error during install: {e}")

# install_playwright_browsers()

# try:
#     import spaces
#     HAS_SPACES = True
# except ImportError:
#     HAS_SPACES = False

# if HAS_SPACES:
#     @spaces.GPU
#     def dummy_gpu():
#         pass  # This makes ZeroGPU happy


# from seo_analyzer import run_seo_analysis_fastapi
# from ai_visibility import run_ai_visibility_analysis

# # ---- Helper functions to format output ----
# def format_seo_result(result):
#     if isinstance(result, tuple):
#         data, csv_path = result
#         if not data:
#             return "No data returned."
#         output = f"## SEO Analysis Results\n"
#         output += f"**Pages analyzed:** {len(data)}\n"
#         scores = [p.get('seo_score', 0) for p in data]
#         avg = sum(scores) / len(scores) if scores else 0
#         output += f"**Average SEO Score:** {avg:.1f}/100\n\n"
#         for i, page in enumerate(data, 1):
#             output += f"### Page {i}: {page.get('url', '')}\n"
#             output += f"- Score: {page.get('seo_score', 0)}/100\n"
#             output += f"- Title: {page.get('title', 'No title')}\n"
#             output += f"- Word Count: {page.get('word_count', 0)}\n"
#             output += f"- H1: {page.get('h1_count', 0)}, H2: {page.get('h2_count', 0)}, H3: {page.get('h3_count', 0)}\n"
#             output += f"- Images: {page.get('total_images', 0)} (missing alt: {page.get('missing_alt_tags', 0)})\n"
#             output += f"- Internal/External links: {page.get('internal_links', 0)}/{page.get('external_links', 0)}\n"
#             if page.get('ai_suggestions'):
#                 output += f"- AI Suggestions: {page['ai_suggestions'][:200]}...\n"
#             output += "\n"
#         return output
#     else:
#         return f"❌ Error: {result.get('message', 'Unknown error')}"

# def format_ai_result(result):
#     if result.get('status') == 'error':
#         return f"❌ Error: {result.get('message', 'Unknown error')}"
#     output = f"## AI Visibility / Readiness Analysis\n"
#     output += f"**URL:** {result.get('url', '')}\n"
#     output += f"**Pages analyzed:** {result.get('pages_analyzed', 0)}\n"
#     output += f"**Overall AI Readiness Score:** {result.get('ai_readiness_score', 0)}/100\n"
#     output += f"**Page types detected:** {result.get('page_type_breakdown', {})}\n\n"
#     cat_scores = result.get('category_scores', {})
#     if cat_scores:
#         output += "### Category Scores\n"
#         for k, v in cat_scores.items():
#             output += f"- {k.replace('_score', '').replace('_', ' ').title()}: {v if v is not None else 'N/A'}\n"
#         output += "\n"
#     previews = result.get('results_preview', [])
#     if previews:
#         output += "### Per-Page Details\n"
#         for p in previews:
#             output += f"**URL:** {p.get('url', '')}\n"
#             output += f"- Page Type: {p.get('page_type', 'unknown')} (conf: {p.get('page_type_confidence', 0):.2f})\n"
#             output += f"- Readiness Score: {p.get('ai_readiness_score', 0)}/100\n"
#             output += f"- Topic Clarity: {p.get('topic_clarity', 0)}\n"
#             output += f"- Content Completeness: {p.get('content_completeness', 0)}\n"
#             output += f"- Entity Clarity: {p.get('entity_clarity', 'N/A')}\n"
#             output += f"- Freshness: {p.get('freshness_status', 'unknown')}\n\n"
#     return output

# # ---- Async analysis wrappers (Gradio will handle async functions) ----
# async def analyze_seo_async(url, max_pages, max_concurrent, use_ai):
#     result = await run_seo_analysis_fastapi(
#         base_url=url,
#         max_pages=int(max_pages),
#         use_ai=use_ai,
#         max_concurrent=int(max_concurrent)
#     )
#     return format_seo_result(result)

# async def analyze_ai_async(url, max_pages, max_concurrent, use_ai):
#     result = await run_ai_visibility_analysis(
#         base_url=url,
#         max_pages=int(max_pages),
#         max_concurrent=int(max_concurrent),
#         use_ai=use_ai
#     )
#     return format_ai_result(result)

# # ---- Gradio Interface ----
# with gr.Blocks(title="SEO & AI Visibility Analyzer") as demo:
#     gr.Markdown("# 🚀 SEO & AI Visibility Analysis Tool")
#     gr.Markdown("Enter a website URL to analyze its SEO health and AI search readiness.")
    
#     with gr.Row():
#         with gr.Column(scale=2):
#             url_input = gr.Textbox(label="Website URL", placeholder="https://example.com", value="https://example.com")
#         with gr.Column(scale=1):
#             max_pages_input = gr.Number(label="Max Pages", value=3, minimum=1, maximum=20, step=1)
#         with gr.Column(scale=1):
#             max_concurrent_input = gr.Number(label="Concurrent Browsers", value=1, minimum=1, maximum=5, step=1)
#         with gr.Column(scale=1):
#             use_ai_check = gr.Checkbox(label="Enable AI Suggestions", value=True)
    
#     with gr.Row():
#         seo_btn = gr.Button("🔍 Analyze SEO", variant="primary")
#         ai_btn = gr.Button("🤖 Analyze AI Visibility", variant="secondary")
    
#     output = gr.Markdown(label="Results")
    
#     seo_btn.click(
#         fn=analyze_seo_async,
#         inputs=[url_input, max_pages_input, max_concurrent_input, use_ai_check],
#         outputs=output
#     )
#     ai_btn.click(
#         fn=analyze_ai_async,
#         inputs=[url_input, max_pages_input, max_concurrent_input, use_ai_check],
#         outputs=output
#     )

# if __name__ == "__main__":
#     demo.launch(server_name="0.0.0.0", server_port=7860)