GigLift: Hinglish job requests in, clear briefs out (Gemma + Django)

说明与用途

GigLift 将非结构化的 WhatsApp 请求转换为结构化的简报，澄清预算、截止日期和交付物，从而简化学生自由职业者的工作流程。

问题

Shop owners in India send informal WhatsApp messages for small jobs, leaving out essential details like budget, deadline, and deliverables, causing confusion and costly revisions for students who act as freelancers.

结果

GigLift successfully transforms informal job requests into structured briefs, improving clarity for both clients and students. Rule-based extraction ensures accurate handling of budgets and deadlines, while the AI generates deliverables and questions. The prototype runs on Render’s free tier and has been evaluated with held-out briefs, achieving high accuracy for budget and deadline extraction.

工作流

• 1. Client pastes WhatsApp-style request into GigLift.
• 2. Core AI module (Gemma) parses the text; rule-based extractor reads any stated budget or deadline and prevents the model from altering them.
• 3. If budget or deadline missing, the model provides a rough guess and prompts the client for clarification.
• 4. The system generates deliverables and open questions based on the request.
• 5. Client reviews and edits the brief; upon publishing, it becomes visible to students.
• 6. Students view ranked gigs, submit proposals, and the client hires one.
• 7. Phone numbers and payment details are revealed only after the hire.

证据

GigLift turns it into a proper brief: what has to be delivered, which skills it needs, a budget, a deadline, and the questions the owner forgot to answer.

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If the key is missing or the call fails, rule-based fallbacks take over and the page says so.

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I wrote 30 briefs by hand (English, Hinglish, Devanagari) and built the rules on them. Then I froze extract.py, whose hash is in eval/FROZEN.txt, wrote 32 new briefs and scored them once.

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The five misses were paanch hazar (an amount in words), 1800 mein ho jaye, due 25 Oct, and within 48 hours. A miss means the app asks the client.

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