Groq Vision OCR & Compound Beta App

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\n High Quality Trivia Assistant\n

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Processing image and answering...

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\n\n \n\n`;\n\n// Serve root with HTML content\napp.get('/', (c) => {\n return c.html(htmlContent);\n});\n\n\n// API endpoint for combining OCR and Compound Beta\napp.post('/api/ask', async (c) => {\n try {\n const formData = await c.req.formData();\n const imageFile = formData.get('image_input');\n\n if (!imageFile || !(imageFile instanceof Blob)) {\n return c.json({ error: 'No image file provided' }, 400);\n }\n\n // Convert blob to base64\n const arrayBuffer = await imageFile.arrayBuffer();\n const bytes = new Uint8Array(arrayBuffer);\n const base64Image = uint8ToBase64(bytes);\n\n // Step 1: OCR\n const ocrResponse = await groq.chat.completions.create({\n model: 'meta-llama/llama-4-scout-17b-16e-instruct',\n messages: [\n {\n role: 'system',\n content: 'You are an expert OCR (Optical Character Recognition) assistant. Your task is to extract ALL text from the provided image. Return ONLY the extracted text without any commentary, introductions, or explanations - just the raw text content that you see in the image, preserving paragraph structure when possible.'\n },\n {\n role: 'user',\n content: [\n {\n type: 'text',\n text: 'Extract all text from this image. Return only the extracted text without any additional commentary.'\n },\n {\n type: 'image_url',\n image_url: {\n url: `data:image/jpeg;base64,${base64Image}`\n }\n }\n ]\n }\n ],\n temperature: 0.2\n });\n\n const extractedText = ocrResponse.choices[0].message.content;\n\n let answer, html;\n let userPrompt, systemPrompt;\n\n systemPrompt = `You are a helpful quiz assistant. You can think aloud and work through the problem, but the final answer should be concise.\nAlways select your answer from the provided choices in the text, making your best guess if it's not clear. \nNever answer \"not above\", \"none of the above\", or similar unless that is explicitly one of the options. \nReturn only the answer choice (e.g., \"B. Paris\" or \"True\" or \"42\"), with no extra commentary.\nIf it is NOT A QUIZ QUESTION, then just summarize or describe the following text as concisely as possible`;\n userPrompt = `Pick the best answer from the choices in this question: ${extractedText}, and start your answer with \"ANSWER:\".`;\n\n const searchResponse = await groq.chat.completions.create({\n model: 'compound-beta-mini',\n messages: [\n {\n role: 'system',\n content: systemPrompt\n },\n {\n role: 'user',\n content: userPrompt\n }\n ],\n temperature: 0.3\n });\n\n answer = searchResponse.choices[0].message.content;\n\n html = marked(answer);\n\n return c.json({ text: answer, html, extractedText });\n } catch (error) {\n console.error('Server error:', error);\n return c.json({ error: 'Server error: ' + error.message }, 500);\n }\n});\n\n// Export app.fetch for Val Town, otherwise export app — this is only for hono apps\nexport default (typeof Deno !== \"undefined\" && Deno.env.get(\"valtown\")) ? app.fetch : app;","deleted","label","hqtrivia","children",[],[99],"files",[152],"counts",{"_175":32,"_176":177,"_178":32,"_179":180},"pulls","branches",1,"forks","envVars",{"_181":177,"_182":32},"count","emptyCount","contents",[],"file",{"_104":153,"_95":99,"_154":116,"_155":-5,"_156":140,"_157":101,"_158":22,"_106":187,"_100":160,"_161":162,"_163":164,"_165":22,"_166":167,"_98":188},["D",1747925232051],[99],"upstreamProject","upstreamBranch","emptyEnvVars","isNew","flags",[],"routes/_app.x.$handle.$name.branch.$branch.version.$version.code.$",{"_93":94,"_95":96,"_97":43,"_100":101,"_98":99,"_102":197,"_150":202,"_171":206,"_185":207,"_183":209,"_210":-5,"_211":-5,"_212":-5,"_213":-5,"_214":215},{"_104":105,"_95":96,"_106":198,"_108":-5,"_109":22,"_110":111,"_112":-5,"_113":-5,"_14":114,"_115":116,"_117":118,"_119":-5,"_120":32,"_16":121,"_122":123,"_124":123,"_125":126,"_127":126,"_128":126,"_129":123,"_130":123,"_23":199,"_134":200,"_137":201,"_149":22},["D",1747925217029],{"_93":94,"_132":133},{"_93":94,"_136":133,"_14":114},{"_100":139,"_95":43,"_104":140,"_141":105,"_142":-5,"_143":-5,"_144":-5,"_145":-5,"_146":-5,"_147":96,"_148":94},[203],{"_104":153,"_95":99,"_154":116,"_155":-5,"_156":140,"_157":101,"_158":22,"_106":159,"_100":160,"_161":162,"_163":164,"_165":22,"_166":167,"_168":204,"_98":205},[],[99],[203],{"_104":153,"_95":99,"_154":116,"_155":-5,"_156":140,"_157":101,"_158":22,"_106":187,"_100":160,"_161":162,"_163":164,"_165":22,"_166":167,"_98":208},[99],[],"upstream","pull","ahead","behind","templates",[],"actionData","errors"]

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