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/chunk_summary.py

@@ -1,4 +1,6 @@
-from bot.models import ReportType
+from bot.models import Depth, FocusArea
+from bot.prompts.depth import DEPTH_CHUNK_MODIFIERS
+from bot.prompts.focus_areas import FOCUS_CHUNK_BULLETS
 
 _BASE = """\
 You are analyzing a batch of Telegram channel posts. Extract structured insights from this chunk.
@@ -10,52 +12,11 @@ Posts:
 {chunk_text}
 """
 
-_CONTENT = """\
-Focus on:
-- Main topics and themes discussed
-- Tone and communication style
-- Content formats (long-form, short updates, lists, etc.)
-- Key narratives or recurring ideas
-- Notable quotes or standout posts
-
-Provide a concise structured summary."""
-
-_CONTENT_STATS = """\
-Focus on:
-- Main topics and themes discussed
-- Tone and communication style
-- Content formats used
-- Key narratives or recurring ideas
-- Posting frequency patterns in this batch
-- Engagement patterns (which topics get more views/forwards/replies)
-- Any notable spikes or drops in engagement
-
-Provide a concise structured summary with both qualitative and quantitative observations."""
-
-_FULL_AUDIT = """\
-Focus on:
-- Main topics and themes discussed
-- Tone and communication style
-- Content formats used
-- Key narratives or recurring ideas
-- Posting frequency patterns
-- Engagement patterns with specific numbers
-- Sentiment analysis (positive/negative/neutral distribution)
-- Audience interaction patterns
-- Content strengths and weaknesses
-- Missed opportunities
-
-Provide a detailed structured summary covering all dimensions."""
-
-CHUNK_PROMPTS = {
-    ReportType.CONTENT: _CONTENT,
-    ReportType.CONTENT_STATS: _CONTENT_STATS,
-    ReportType.FULL_AUDIT: _FULL_AUDIT,
-}
-
 
 def build_chunk_prompt(
-    report_type: ReportType,
+    depth: Depth,
+    focus_areas: list[FocusArea],
+    lang: str,
     title: str,
     chunk_text: str,
     chunk_idx: int,
@@ -67,4 +28,14 @@ def build_chunk_prompt(
         total_chunks=total_chunks,
         chunk_text=chunk_text,
     )
-    return base + "\n" + CHUNK_PROMPTS[report_type]
+    parts = [base]
+
+    parts.append(f"Depth: {DEPTH_CHUNK_MODIFIERS[depth]}\n")
+
+    parts.append("Focus on the following areas:")
+    for area in focus_areas:
+        parts.append(FOCUS_CHUNK_BULLETS[area])
+
+    parts.append(f"\nProvide the analysis in {'Russian' if lang == 'ru' else 'English'}.")
+
+    return "\n".join(parts)