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{ OpenAI } from \"https://esm.town/v/std/openai\";\nimport { sqlite } from \"https://esm.town/v/std/sqlite\";\nimport { discordWebhook } from \"https://esm.town/v/stevekrouse/discordWebhook\";\nimport { z } from \"npm:zod\";\n\nconst SOCIAL_DATA_API_KEY = Deno.env.get(\"SOCIAL_DATA_API_KEY\") || \"\";\n\n// Initialize SQLite database\nawait sqlite.execute(`\n CREATE TABLE IF NOT EXISTS cabinAdjacentTweets (\n tweetId TEXT PRIMARY KEY,\n processedAt TEXT NOT NULL,\n tweet_text TEXT NOT NULL\n )\n`);\n\n// Types\ninterface Tweet {\n id: number;\n id_str: string;\n text: string | null;\n full_text: string;\n tweet_created_at: string;\n source: string;\n truncated: boolean;\n in_reply_to_status_id: number | null;\n in_reply_to_status_id_str: string | null;\n in_reply_to_user_id: number | null;\n in_reply_to_user_id_str: string | null;\n in_reply_to_screen_name: string | null;\n user: {\n id: number;\n id_str: string;\n name: string;\n screen_name: string;\n location: string;\n url: string;\n description: string;\n protected: boolean;\n verified: boolean;\n followers_count: number;\n friends_count: number;\n listed_count: number;\n favourites_count: number;\n statuses_count: number;\n created_at: string;\n profile_banner_url: string;\n profile_image_url_https: string;\n can_dm: boolean;\n };\n quoted_status_id: number | null;\n quoted_status_id_str: string | null;\n is_quote_status: boolean;\n quoted_status: any | null;\n retweeted_status: any | null;\n quote_count: number;\n reply_count: number;\n retweet_count: number;\n favorite_count: number;\n views_count: number;\n bookmark_count: number;\n lang: string;\n}\n\ninterface Assessment {\n isRelevant: boolean;\n}\n\nconst webhooks: Record = {\n \"gg23-main\": Deno.env.get(\"GG23_MAIN_WEBHOOK\") || \"\",\n \"off-topic\": Deno.env.get(\"GG23_FILTERED_OUT_WEBHOOK\") || \"\",\n};\n\nconst trackedUsernames = [];\n\nconst openai = new OpenAI();\n\n// Define Zod schemas for validation\nconst MessageSchema = z.object({\n content: z.string(),\n});\n\nconst ChoiceSchema = z.object({\n message: MessageSchema,\n});\n\nconst CompletionSchema = z.object({\n choices: z.array(ChoiceSchema).min(1),\n});\n\nconst AssessmentSchema = z.object({\n isRelevant: z.boolean(),\n});\n\nasync function isTweetProcessed(tweetId: string): Promise {\n const result = await sqlite.execute({\n sql: \"SELECT tweetId FROM cabinAdjacentTweets WHERE tweetId = :tweetId\",\n args: { tweetId },\n });\n return result.rows.length > 0;\n}\n\nasync function markTweetAsProcessed(tweetId: string, tweetText: string): Promise {\n await sqlite.execute({\n sql:\n \"INSERT INTO cabinAdjacentTweets (tweetId, processedAt, tweet_text) VALUES (:tweetId, :processedAt, :tweetText)\",\n args: {\n tweetId,\n processedAt: new Date().toISOString(),\n tweetText,\n },\n });\n}\n\n// Core API functions\nasync function searchTweets(query: string, startTime: Date): Promise {\n try {\n console.log(`Making request to socialdata API with query: ${query}`);\n const url = `https://api.socialdata.tools/twitter/search?query=${encodeURIComponent(query)}&type=Latest`;\n console.log(`URL: ${url}`);\n\n const response = await fetch(url, {\n headers: {\n \"Authorization\": `Bearer ${SOCIAL_DATA_API_KEY}`,\n \"Accept\": \"application/json\",\n },\n });\n\n console.log(\"Response status:\", response.status);\n // console.log(\"Response headers:\", Object.fromEntries(response.headers));\n\n if (!response.ok) {\n const errorText = await response.text();\n console.error(`HTTP error! status: ${response.status}, body: ${errorText}`);\n return [];\n }\n\n const data = await response.json();\n console.log(`Successfully received ${data.tweets?.length || 0} tweets`);\n\n return (data.tweets || [])\n .map((tweet: any) => ({\n id: tweet.id,\n id_str: tweet.id_str,\n text: tweet.text,\n full_text: tweet.full_text,\n tweet_created_at: tweet.tweet_created_at,\n source: tweet.source,\n truncated: tweet.truncated,\n in_reply_to_status_id: tweet.in_reply_to_status_id,\n in_reply_to_status_id_str: tweet.in_reply_to_status_id_str,\n in_reply_to_user_id: tweet.in_reply_to_user_id,\n in_reply_to_user_id_str: tweet.in_reply_to_user_id_str,\n in_reply_to_screen_name: tweet.in_reply_to_screen_name,\n user: tweet.user,\n quoted_status_id: tweet.quoted_status_id,\n quoted_status_id_str: tweet.quoted_status_id_str,\n is_quote_status: tweet.is_quote_status,\n quoted_status: tweet.quoted_status,\n retweeted_status: tweet.retweeted_status,\n quote_count: tweet.quote_count,\n reply_count: tweet.reply_count,\n retweet_count: tweet.retweet_count,\n favorite_count: tweet.favorite_count,\n views_count: tweet.views_count,\n bookmark_count: tweet.bookmark_count,\n lang: tweet.lang,\n }));\n } catch (error) {\n console.error(\"Error searching tweets:\", {\n name: error.name,\n message: error.message,\n stack: error.stack,\n });\n return [];\n }\n}\n\nasync function getUserTweets(username: string, startTime: Date): Promise {\n try {\n // For user tweets, we can use the search endpoint with from: operator\n const query = `from:${username}`;\n const url = `https://api.socialdata.tools/twitter/search?query=${encodeURIComponent(query)}&type=Latest`;\n console.log(`Making request to socialdata API with query: ${query}`, url);\n\n const response = await fetch(url, {\n headers: {\n \"Authorization\": `Bearer ${SOCIAL_DATA_API_KEY}`,\n \"Accept\": \"application/json\",\n },\n });\n // console.log({ response });\n // console.log(JSON.stringify(response, null, 2));\n\n if (!response.ok) {\n console.error(`Failed to get tweets for ${username}`);\n return [];\n }\n\n const data = await response.json();\n // console.log({ data });\n console.log(`Successfully received ${data.tweets?.length || 0} tweets`);\n\n return (data.tweets || [])\n .filter((tweet: any) => new Date(tweet.tweet_created_at) >= startTime)\n .map((tweet: any) => ({\n id: tweet.id,\n id_str: tweet.id_str,\n text: tweet.text,\n full_text: tweet.full_text,\n tweet_created_at: tweet.tweet_created_at,\n source: tweet.source,\n truncated: tweet.truncated,\n in_reply_to_status_id: tweet.in_reply_to_status_id,\n in_reply_to_status_id_str: tweet.in_reply_to_status_id_str,\n in_reply_to_user_id: tweet.in_reply_to_user_id,\n in_reply_to_user_id_str: tweet.in_reply_to_user_id_str,\n in_reply_to_screen_name: tweet.in_reply_to_screen_name,\n user: tweet.user,\n quoted_status_id: tweet.quoted_status_id,\n quoted_status_id_str: tweet.quoted_status_id_str,\n is_quote_status: tweet.is_quote_status,\n quoted_status: tweet.quoted_status,\n retweeted_status: tweet.retweeted_status,\n quote_count: tweet.quote_count,\n reply_count: tweet.reply_count,\n retweet_count: tweet.retweet_count,\n favorite_count: tweet.favorite_count,\n views_count: tweet.views_count,\n bookmark_count: tweet.bookmark_count,\n lang: tweet.lang,\n }));\n } catch (error) {\n console.error(`Error fetching tweets for ${username}:`, error);\n return [];\n }\n}\n\n// Search functions\nconst KEYWORD_CHUNKS = [\n \"\\\"open source community tool\\\" OR \\\"neighborhood software\\\" OR \\\"local public good\\\" OR \\\"community software\\\" OR \\\"hyperlocal\\\" OR \\\"community organizing\\\" OR \\\"neighborhood tool\\\" OR \\\"local tooling\\\"\",\n];\n\nasync function scanKeywords(startTime: Date): Promise {\n console.log(\"Starting keyword scan...\");\n const allTweets: Tweet[] = [];\n\n for (const queryChunk of KEYWORD_CHUNKS) {\n console.log(`Processing query chunk: ${queryChunk}`);\n const tweets = await searchTweets(queryChunk, startTime);\n allTweets.push(...tweets);\n\n // Add a small delay between chunks to avoid rate limiting\n await new Promise(resolve => setTimeout(resolve, 1000));\n }\n\n // Remove any duplicate tweets that might have matched multiple queries\n const uniqueTweets = Array.from(new Map(allTweets.map(tweet => [tweet.id, tweet])).values());\n console.log(`Found ${uniqueTweets.length} unique tweets from ${allTweets.length} total matches`);\n\n return uniqueTweets;\n}\n\nasync function scanAccounts(startTime: Date): Promise {\n console.log(\"Starting account scan...\");\n const BATCH_SIZE = 5;\n const allTweets: Tweet[] = [];\n\n for (let i = 0; i < trackedUsernames.length; i += BATCH_SIZE) {\n const userBatch = trackedUsernames.slice(i, i + BATCH_SIZE);\n console.log(`Processing batch of ${userBatch.length} users...`);\n\n const batchResults = await Promise.all(\n userBatch.map(username => getUserTweets(username, startTime)),\n );\n\n allTweets.push(...batchResults.flat());\n\n // Add delay between batches to avoid rate limits\n if (i + BATCH_SIZE < trackedUsernames.length) {\n await new Promise(resolve => setTimeout(resolve, 2000));\n }\n }\n\n return allTweets;\n}\n\n// Retry helper with exponential backoff\nasync function retryWithBackoff(\n operation: () => Promise,\n maxRetries: number = 5,\n initialDelayMs: number = 1000,\n): Promise {\n let retries = 0;\n while (true) {\n try {\n return await operation();\n } catch (error) {\n if (error?.status === 429 && retries < maxRetries) {\n // Get retry delay from response headers or use exponential backoff\n const retryAfter = parseInt(error?.headers?.[\"x-ratelimit-reset\"] || \"0\") * 1000;\n const backoffDelay = retryAfter || initialDelayMs * Math.pow(2, retries);\n console.log(`Rate limited. Retrying in ${backoffDelay}ms (attempt ${retries + 1}/${maxRetries})`);\n await new Promise(resolve => setTimeout(resolve, backoffDelay));\n retries++;\n } else {\n throw error;\n }\n }\n }\n}\n\n// Assessment and processing\nasync function assessTweet(tweet: string): Promise {\n try {\n const completion = await retryWithBackoff(() =>\n openai.chat.completions.create({\n model: \"gpt-4o-mini\",\n messages: [\n {\n role: \"system\",\n content: `You are a classifier for tweets related to the Gitcoin neighborhood software grant round.\n\nA tweet is relevant if it:\n1. Mentions open-source software projects for community/neighborhood use\n2. Refers to community tools, neighborhood software, or local public goods\n3. Discusses grants/funding for community or neighborhood software\n4. References or shares links about community software grant opportunities\n5. Promotes open-source tools for neighborhood or local communities\n6. Mentions hyperlocal software or community organizing tools\n7. Recommends or asks for community software solutions\n\nEven if a tweet only briefly mentions community software or grants for it, consider it relevant.\n\nImportant: If the tweet mentions open-source community software AND grants/funding in any way, it is almost certainly relevant.\n\nRespond with a JSON object containing only: {\"isRelevant\": true/false}`,\n },\n {\n role: \"user\",\n content: tweet,\n },\n ],\n })\n );\n\n // Parse completion with Zod\n let response = \"\";\n try {\n const validatedCompletion = CompletionSchema.parse(completion);\n response = validatedCompletion.choices[0].message.content;\n console.log(\"gpt response\", response, tweet);\n } catch (validationError) {\n console.error(\"Failed to validate OpenAI response:\", validationError);\n console.log(\"Raw completion:\", completion);\n return { isRelevant: false };\n }\n\n // Strip out markdown code block syntax if present\n const jsonStr = response.replace(/^```json\\n|\\n```$/g, \"\").trim();\n\n try {\n // Parse and validate the assessment result\n const result = AssessmentSchema.parse(JSON.parse(jsonStr));\n console.log(`Parsed assessment result: ${JSON.stringify(result)}`);\n return result;\n } catch (parseError) {\n console.error(`Error parsing GPT response as JSON: ${parseError.message}`);\n console.error(`Raw response: ${jsonStr}`);\n return { isRelevant: false };\n }\n } catch (error) {\n console.error(\"Error assessing tweet:\", error);\n return {\n isRelevant: false,\n };\n }\n}\n\nasync function sendToDiscord(tweet: Tweet, webhookUrl: string): Promise {\n const content = `https://x.com/${tweet.user.screen_name}/status/${tweet.id_str}`;\n await discordWebhook({\n url: webhookUrl,\n content,\n });\n}\n\n// Main processing pipeline\nasync function processTweets(tweets: Tweet[]) {\n for (const tweet of tweets) {\n try {\n // Check if tweet was already processed\n if (await isTweetProcessed(tweet.id_str)) {\n console.log(`Tweet ${tweet.id_str} already processed, skipping...`);\n continue;\n }\n\n const assessment = await assessTweet(tweet.full_text);\n console.log(`Tweet ${tweet.id_str} assessment result: ${assessment.isRelevant}`);\n\n await markTweetAsProcessed(tweet.id_str, tweet.full_text);\n\n if (assessment.isRelevant) {\n const webhookUrl = webhooks[\"gg23-main\"];\n if (webhookUrl) {\n await sendToDiscord(tweet, webhookUrl);\n console.log(`Sent relevant tweet ${tweet.id_str} to main webhook`);\n } else {\n console.log(\"No webhook URL found for category: gg23-main\");\n }\n } else {\n console.log(\"Tweet not relevant\", tweet.id_str);\n await sendToDiscord(tweet, webhooks[\"off-topic\"]);\n }\n } catch (error) {\n console.error(`Error processing tweet ${tweet.id_str}:`, error);\n }\n }\n}\n\n// Main function\nexport async function twitterAlert({ lastRunAt }: { lastRunAt: string }) {\n console.log(\"Starting Twitter alert scan...\");\n const startTime = new Date(Date.parse(lastRunAt) - 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