Sergei Poljanski
c30b7e4675
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
74 lines
2.9 KiB
Python
74 lines
2.9 KiB
Python
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."
|
|
),
|
|
}
|