After running an event, the feedback collection process is almost always broken. You get a spreadsheet of raw responses, manually tally the ratings, write a summary email to the organizer, and hope you didn't miss anything important. It's tedious, slow, and doesn't scale.
I built a full-stack AI-powered event feedback system that automates this entire pipeline end-to-end — from the moment an attendee submits feedback to a branded PDF report landing in the organizer's inbox, complete with AI-generated sentiment analysis and improvement suggestions.
What the System Does
Every feedback submission triggers a fully automated chain:
- Form data is cleaned and validated by the FastAPI backend
- Gemini 2.5 analyzes the text for sentiment, urgency, highlights, and suggestions
- A branded HTML report is generated and converted to PDF
- The PDF is emailed to the event organizer via Gmail SMTP
- All data (including AI output) is persisted to a Supabase PostgreSQL database
- The analytics dashboard reflects the new submission in real time
System Architecture
Form Submission
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Data Cleaning & Normalization
│
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Gemini 2.5 AI Analysis
│
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HTML Report Generation (Branded)
│
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PDF Conversion
│
├──▶ SMTP Email to Organizer
│
└──▶ Supabase Database Storage
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Dashboard & Analytics
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Date-Range Bulk Report (PDF + Email)
Tech Stack
| Layer | Technology |
|---|---|
| Frontend | Next.js + Tailwind CSS |
| Backend | FastAPI (Python) |
| AI Analysis | Google Gemini 2.5 |
| Gmail SMTP (TLS, Port 587) | |
| PDF Generation | HTML-to-PDF conversion |
| Database | Supabase (PostgreSQL) |
The AI Analysis Layer: Gemini 2.5
The most interesting part of this system is the AI pipeline. Rather than using a generic sentiment library, I integrated Google Gemini 2.5 directly to generate structured, actionable intelligence from each feedback submission.
The prompt is designed to return a consistent JSON structure every time:
{
"sentiment": "Positive | Neutral | Negative",
"summary": "2-3 sentence summary of the feedback",
"highlights": ["key point 1", "key point 2"],
"improvementSuggestions": ["suggestion 1", "suggestion 2"],
"urgency": "Low | Medium | High"
}
The urgency field is particularly useful — it lets organizers immediately identify submissions that need follow-up action, without reading every comment manually.
Tip: When prompting Gemini for structured JSON output, always explicitly specify the expected schema in your prompt and validate the response server-side. LLMs can occasionally deviate from the schema under edge cases.
📄 Automated PDF Report Generation
After AI analysis, the backend generates a branded HTML report and converts it to PDF. The report includes:
- Event metadata (name, date, organizer)
- Attendee information
- Star rating visualization
- AI-generated summary and highlights
- Improvement suggestions with urgency indicator
- Submission ID and timestamp for traceability
Using HTML-to-PDF conversion rather than a PDF library gives you full control over visual design — CSS, fonts, colors, layout — without being constrained by a library's API.
SMTP Email Delivery
The PDF report is automatically emailed to the event organizer via Gmail SMTP:
# Email config
SMTP_HOST = "smtp.gmail.com"
SMTP_PORT = 587
SMTP_SECURITY = "TLS"
# The SMTP password is stored in .env — never hardcoded
Important: Gmail requires an App Password (not your account password) when authenticating via SMTP with 2FA enabled. Store it exclusively in environment secrets.
Database Schema: Supabase
All submissions and AI outputs are persisted to a Supabase PostgreSQL table (feedback_reports). The schema captures everything — raw input, AI analysis fields, PDF URL, and email delivery status — in a single row per submission, making dashboard queries simple and fast.
Key fields: submissionId, eventName, eventDate, rating, sentiment, summary, urgency, highlights, improvementSuggestions, pdfUrl, emailSent.
The Analytics Dashboard
The Next.js dashboard connects to the API and provides:
- Real-time submission counts and average ratings
- Sentiment breakdown (Positive / Neutral / Negative)
- High-urgency submission highlighting
- Filterable table with date range, event name, and sentiment filters
- Per-submission PDF access links
The bulk report feature is especially powerful: select a date range, and the system retrieves all submissions in that window, passes the full dataset to Gemini for a consolidated analysis, generates a combined PDF, and emails it to the admin. A multi-hour manual reporting task reduced to a single button click.
Security Considerations
- SMTP credentials stored exclusively in environment variables — never in the codebase
- Supabase connection credentials managed via env secrets
- Input validation at the FastAPI layer using Pydantic before data reaches AI or storage
- Gemini API key scoped to the backend only — never exposed to the client
Getting Started
git clone https://github.com/Sameershahh/feedback-form-system
cd feedback-form-system
# Backend
cd api
pip install -r requirements.txt
# Add .env with Supabase, Gmail SMTP, and Gemini credentials
uvicorn main:app --reload
# Frontend
cd ../frontend
npm install
npm run dev
Real-World Use Cases
- Conference and workshop organizers collecting post-event feedback
- Corporate training teams tracking session quality
- SaaS products embedding feedback collection into onboarding flows
- Event agencies needing automated client-ready reports
Resources
Curious about the AI prompt engineering behind the Gemini integration, or how the bulk report aggregation works? Drop a comment — happy to go deeper on any part of the stack.
Built by Sameer Shah — AI & Full-Stack Developer | Portfolio