Add multi-user features: i18n, payments, multi-model AI, focus areas

Replace single report type with composable analysis: depth levels
(basic/standard/full) + focus area multi-select (psychology, business,
marketing, content, audience, sentiment). Multi-step inline keyboard
flow guides users through selection.

- i18n: English + Russian, auto-detect from Telegram, /lang override
- AI providers: Anthropic + OpenRouter via AIClient abstraction
- Telegram Stars payments with per-depth pricing and free trial
- SQLite (aiosqlite) for users, analyses, payments tracking
- User middleware for auto-registration and language detection
- Report persistence: save .md locally, offer file download
- New commands: /features, /prices, /lang
- Composable prompt system: depth modifiers + focus area fragments

bot/prompts/focus_areas.py

@@ -0,0 +1,74 @@
+from bot.models import FocusArea
+
+FOCUS_CHUNK_BULLETS: dict[FocusArea, str] = {
+    FocusArea.PSYCHOLOGY: (
+        "- Persuasion and influence techniques used\n"
+        "- Cognitive biases leveraged (scarcity, social proof, authority, etc.)\n"
+        "- Emotional triggers and manipulation patterns\n"
+        "- Framing and narrative control techniques"
+    ),
+    FocusArea.BUSINESS: (
+        "- Revenue models and monetization strategies\n"
+        "- Product/service placement and promotion patterns\n"
+        "- Conversion funnels and calls-to-action\n"
+        "- Pricing psychology and offer structuring"
+    ),
+    FocusArea.MARKETING: (
+        "- Growth tactics and audience acquisition strategies\n"
+        "- Viral mechanics and shareability factors\n"
+        "- Cross-promotion and collaboration patterns\n"
+        "- Brand positioning and differentiation"
+    ),
+    FocusArea.CONTENT: (
+        "- Main topics and themes discussed\n"
+        "- Content formats (long-form, short updates, lists, media, etc.)\n"
+        "- Posting frequency and schedule patterns\n"
+        "- Narrative arcs and series/recurring segments\n"
+        "- Content quality and originality assessment"
+    ),
+    FocusArea.AUDIENCE: (
+        "- Engagement patterns (views, forwards, replies per content type)\n"
+        "- Audience interaction and community dynamics\n"
+        "- Top-performing vs underperforming content\n"
+        "- Engagement drivers and detractors"
+    ),
+    FocusArea.SENTIMENT: (
+        "- Overall sentiment distribution (positive/negative/neutral)\n"
+        "- Sentiment breakdown by topic\n"
+        "- Emotional tone shifts over time\n"
+        "- Controversial or polarizing content identification"
+    ),
+}
+
+FOCUS_SYNTHESIS_SECTIONS: dict[FocusArea, str] = {
+    FocusArea.PSYCHOLOGY: (
+        "## Psychology & Influence\n"
+        "Analyze persuasion techniques, cognitive biases, emotional triggers, "
+        "and manipulation patterns found across the channel's content."
+    ),
+    FocusArea.BUSINESS: (
+        "## Business & Monetization\n"
+        "Detail revenue models, monetization strategies, product placements, "
+        "conversion patterns, and business-related content."
+    ),
+    FocusArea.MARKETING: (
+        "## Marketing & Growth\n"
+        "Assess growth tactics, viral mechanics, cross-promotion strategies, "
+        "and brand positioning approaches."
+    ),
+    FocusArea.CONTENT: (
+        "## Content Strategy\n"
+        "Analyze topics, formats, posting patterns, narrative arcs, "
+        "content quality, and overall editorial strategy."
+    ),
+    FocusArea.AUDIENCE: (
+        "## Audience & Engagement\n"
+        "Deep dive into engagement metrics, audience interaction patterns, "
+        "community dynamics, and what drives or kills engagement."
+    ),
+    FocusArea.SENTIMENT: (
+        "## Sentiment Analysis\n"
+        "Present sentiment distribution, emotional tone analysis, "
+        "sentiment by topic, and shifts over time."
+    ),
+}