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Update app.py
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app.py
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@@ -105,11 +105,6 @@ class GEOSEOApp:
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st.sidebar.markdown("- π Website GEO Analysis")
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st.sidebar.markdown("- π AI-First SEO Scoring")
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st.sidebar.markdown("---")
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st.sidebar.markdown("### π§ Configuration")
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st.sidebar.markdown("Set your API keys:")
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st.sidebar.code("export GROQ_API_KEY='your-key'")
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st.sidebar.markdown("---")
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st.sidebar.markdown("### π GEO Metrics")
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st.sidebar.markdown("**AI Search Visibility**: How likely AI engines will surface your content")
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@@ -148,19 +143,19 @@ class GEOSEOApp:
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"Select GEO Optimization Type:",
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options=[
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"geo_standard",
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"competitive_geo",
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"geo_readability",
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"geo_entity_extraction",
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"geo_variations",
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"geo_batch_optimize"
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],
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format_func=lambda x: {
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"geo_standard": "π§ Standard GEO Enhancement",
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"competitive_geo": "π Competitive GEO Analysis",
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"geo_readability": "π GEO Readability Analysis",
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"geo_entity_extraction": "π·οΈ GEO Entity Extraction",
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"geo_variations": "π GEO Content Variations",
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"geo_batch_optimize": "π¦ Batch GEO Optimization"
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}[x],
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index=0,
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help="Choose the type of GEO optimization powered by RAG analysis"
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@@ -171,14 +166,14 @@ class GEOSEOApp:
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if optimization_type in ["geo_standard", "competitive_geo"]:
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analyze_only = st.checkbox("Analysis only (no rewriting)", value=False)
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include_rag_context = st.checkbox("Include RAG context details", value=True)
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elif optimization_type == "geo_variations":
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elif optimization_type == "geo_batch_optimize":
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else:
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analyze_only = False
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include_rag_context = True
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@@ -186,11 +181,11 @@ class GEOSEOApp:
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# Show description based on optimization type
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optimization_descriptions = {
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"geo_standard": "π§ RAG-enhanced GEO optimization focusing on AI search visibility, conversational readiness, and citation worthiness using knowledge base guidance.",
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"competitive_geo": "π Competitive GEO analysis against best practices with gap identification and actionable recommendations using RAG context.",
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"geo_readability": "π Detailed readability analysis specifically optimized for AI systems and LLM consumption patterns.",
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"geo_entity_extraction": "π·οΈ AI-powered extraction of key entities, topics, and concepts relevant for GEO optimization.",
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"geo_variations": "π Generate multiple GEO-optimized variations (FAQ, conversational, authoritative) using RAG knowledge.",
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"geo_batch_optimize": "π¦ Process multiple content pieces simultaneously with consistent GEO optimization."
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}
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st.info(f"**{optimization_descriptions[optimization_type]}**")
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st.sidebar.markdown("- π Website GEO Analysis")
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st.sidebar.markdown("- π AI-First SEO Scoring")
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st.sidebar.markdown("---")
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st.sidebar.markdown("### π GEO Metrics")
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st.sidebar.markdown("**AI Search Visibility**: How likely AI engines will surface your content")
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"Select GEO Optimization Type:",
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options=[
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"geo_standard",
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# "competitive_geo",
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# "geo_readability",
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# "geo_entity_extraction",
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# "geo_variations",
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# "geo_batch_optimize"
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],
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format_func=lambda x: {
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"geo_standard": "π§ Standard GEO Enhancement",
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# "competitive_geo": "π Competitive GEO Analysis",
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# "geo_readability": "π GEO Readability Analysis",
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# "geo_entity_extraction": "π·οΈ GEO Entity Extraction",
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# "geo_variations": "π GEO Content Variations",
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# "geo_batch_optimize": "π¦ Batch GEO Optimization"
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}[x],
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index=0,
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help="Choose the type of GEO optimization powered by RAG analysis"
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if optimization_type in ["geo_standard", "competitive_geo"]:
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analyze_only = st.checkbox("Analysis only (no rewriting)", value=False)
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include_rag_context = st.checkbox("Include RAG context details", value=True)
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# elif optimization_type == "geo_variations":
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# num_variations = st.slider("Number of variations", min_value=1, max_value=3, value=2)
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# analyze_only = False
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# include_rag_context = True
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# elif optimization_type == "geo_batch_optimize":
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# st.info("For batch optimization, separate multiple content pieces with '---' divider")
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# analyze_only = False
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# include_rag_context = True
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else:
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analyze_only = False
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include_rag_context = True
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# Show description based on optimization type
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optimization_descriptions = {
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"geo_standard": "π§ RAG-enhanced GEO optimization focusing on AI search visibility, conversational readiness, and citation worthiness using knowledge base guidance.",
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# "competitive_geo": "π Competitive GEO analysis against best practices with gap identification and actionable recommendations using RAG context.",
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# "geo_readability": "π Detailed readability analysis specifically optimized for AI systems and LLM consumption patterns.",
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# "geo_entity_extraction": "π·οΈ AI-powered extraction of key entities, topics, and concepts relevant for GEO optimization.",
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# "geo_variations": "π Generate multiple GEO-optimized variations (FAQ, conversational, authoritative) using RAG knowledge.",
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# "geo_batch_optimize": "π¦ Process multiple content pieces simultaneously with consistent GEO optimization."
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}
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st.info(f"**{optimization_descriptions[optimization_type]}**")
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