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FastAPI + CrewAI (Python) — deploys to Render\n│   ├── app.py                    # FastAPI app: /run, /status, /history, /report, /download\n│   ├── src/market_research_crew/ # CrewAI agents, tasks, and Groq LLM config\n│   ├── report/                   # Generated reports are saved here (git-ignored)\n│   ├── requirements.txt\n│   ├── pyproject.toml\n│   ├── Procfile                  # `web: uvicorn app:app --host 0.0.0.0 --port $PORT`\n│   ├── .python-version\n│   └── .env.example\n│\n├── frontend/                     # React + Vite (JavaScript) — deploys to Vercel\n│   ├── src/\n│   │   ├── App.jsx               # Main UI (ported 1:1 from the original design)\n│   │   ├── api.js                # Talks to the backend via VITE_API_URL\n│   │   └── App.css               # Original stylesheet (dark/light mode)\n│   ├── package.json\n│   ├── vercel.json\n│   └── .env.example\n│\n├── render.yaml                   # Optional Render Blueprint for one-click backend deploy\n└── README.md\n```\n\nThe frontend never talks to the LLM directly — it only calls the FastAPI backend, which runs the CrewAI pipeline and calls Groq's API on the server side. Your Groq API key is never exposed to the browser.\n\n---\n\n## 🔑 Prerequisites\n\n- **Python** ≥ 3.10, < 3.14\n- **Node.js** ≥ 18 and **npm**\n- A **[Groq API key](https://console.groq.com/keys)** (free tier available)\n- *(Optional)* A **[Serper.dev](https://serper.dev) API key** (if omitted, the app uses **DuckDuckGo** — 100% free with no key required)\n\n---\n\n## 🚀 Getting Started (Local Development)\n\nYou'll run two servers in two separate terminals: the FastAPI backend (port `8000`) and the React dev server (port `5173`).\n\n### 1. Backend — FastAPI\n\n```bash\ncd backend\n\n# Create and activate a virtual environment\npython -m venv .venv\nsource .venv/bin/activate      # Windows: .venv\\Scripts\\activate\n\n# Install dependencies\npip install -r requirements.txt\n# (equivalently, if you use uv: uv pip install -r requirements.txt)\n\n# Set up environment variables\ncp .env.example .env\n```\n\nEdit `backend/.env` and fill in your Groq key:\n\n```env\nGROQ_API_KEY=your_groq_api_key_here\nMODEL=qwen/qwen3.8-27b\n# SERPER_API_KEY is optional (leave blank for free DuckDuckGo search)\nSERPER_API_KEY=\nCORS_ORIGINS=http://localhost:5173,http://127.0.0.1:5173\n```\n\n> Groq periodically deprecates older models (e.g. `llama-3.3-70b-versatile` has been retired). Check [console.groq.com/docs/models](https://console.groq.com/docs/models) for the current list if `MODEL` stops working.\n\nRun the API server:\n\n```bash\nuvicorn app:app --reload --host 0.0.0.0 --port 8000\n```\n\nThe API is now running at **http://localhost:8000** (interactive docs at `http://localhost:8000/docs`).\n\n### 2. Frontend — React\n\nIn a **second terminal**:\n\n```bash\ncd frontend\n\n# Install dependencies\nnpm install\n\n# Set up environment variables\ncp .env.example .env\n```\n\n`frontend/.env` should point at your local backend:\n\n```env\nVITE_API_URL=http://localhost:8000\n```\n\nRun the dev server:\n\n```bash\nnpm run dev\n```\n\nOpen **http://localhost:5173** in your browser. 🎉\n\n---\n\n## 🔑 Environment Variables\n\n### Backend (`backend/.env`)\n\n| Variable | Description | Required |\n|----------|-------------|----------|\n| `GROQ_API_KEY` | [Groq](https://console.groq.com/keys) API key used by every AI agent | ✅ |\n| `MODEL` | Groq model name (e.g. `qwen/qwen3.8-27b`, `openai/gpt-oss-120b`) | ✅ |\n| `SERPER_API_KEY` | [Serper.dev](https://serper.dev) API key (defaults to free DuckDuckGo if omitted) | ❌ |\n| `CORS_ORIGINS` | Comma-separated list of origins allowed to call the API | ❌ (defaults to `http://localhost:5173`) |\n\n### Frontend (`frontend/.env`)\n\n| Variable | Description | Required |\n|----------|-------------|----------|\n| `VITE_API_URL` | Base URL of the FastAPI backend | ✅ (defaults to `http://localhost:8000`) |\n\n> **Never commit `.env` files.** Both are already excluded via `.gitignore`; only the `.env.example` templates are committed.\n\n---\n\n## 🖥️ Features\n\n- **Modern Web UI** — Clean, responsive interface with dark/light mode toggle\n- **Real-time Progress** — Watch each AI agent activate as the pipeline progresses\n- **Report History** — Browse and revisit previously generated reports\n- **Export Options** — Download reports as `.md` or `.pdf` (via print), or copy to clipboard\n- **Background Processing** — Reports generate asynchronously so the UI stays responsive\n\n---\n\n## 🛠️ Tech Stack\n\n| Layer | Technology |\n|-------|------------|\n| **AI Framework** | [CrewAI](https://crewai.com) — Multi-agent orchestration |\n| **LLM** | [Groq](https://groq.com) (configurable via `MODEL`) |\n| **Web Search** | [Serper.dev](https://serper.dev) API |\n| **Backend** | [FastAPI](https://fastapi.tiangolo.com) (Python) |\n| **Frontend** | [React](https://react.dev) + [Vite](https://vitejs.dev) |\n\n---\n\n## 📝 Usage\n\n1. Make sure both the backend (`:8000`) and frontend (`:5173`) are running\n2. Open `http://localhost:5173` in your browser\n3. Type or paste your **product/startup idea** in the text box\n4. Click **\"Generate Report\"**\n5. Watch the 5 AI agents process your idea in real time (~3–8 minutes)\n6. View, copy, or download the finished report\n\n---\n\n## ☁️ Deployment (GitHub → Render → Vercel)\n\nThe backend deploys to **Render** (Python web service) and the frontend deploys to **Vercel** (static Vite build). They're pushed as one GitHub repo but deployed as two separate services, connected via `VITE_API_URL` / `CORS_ORIGINS`.\n\n### 1. Push to GitHub\n\n```bash\ngit init\ngit add .\ngit commit -m \"Initial commit\"\ngit branch -M main\ngit remote add origin https://github.com/<your-username>/<your-repo>.git\ngit push -u origin main\n```\n\n`git status` first to confirm no `.env` file is staged — it's excluded by `.gitignore`, but it's worth double-checking before your first push.\n\n### 2. Deploy the backend on Render\n\n1. Go to [dashboard.render.com](https://dashboard.render.com) → **New** → **Web Service**.\n2. Connect your GitHub account and select this repository.\n3. Configure the service:\n   - **Root Directory**: `backend`\n   - **Runtime**: Python 3\n   - **Build Command**: `pip install -r requirements.txt`\n   - **Start Command**: `uvicorn app:app --host 0.0.0.0 --port $PORT`\n   - **Instance Type**: Free (or paid, for less cold-start latency)\n4. Add environment variables under **Environment**:\n   - `GROQ_API_KEY` = your real Groq key\n   - `MODEL` = e.g. `qwen/qwen3.8-27b`\n   - `SERPER_API_KEY` = (optional)\n   - `CORS_ORIGINS` = `http://localhost:5173` for now — you'll update this after step 3\n5. Click **Create Web Service**. Render will build and deploy; note the resulting URL, e.g. `https://market-research-backend.onrender.com`.\n6. Sanity-check it: open `https://<your-backend>.onrender.com/` in a browser — you should see `{\"status\": \"ok\", ...}`.\n\n> A `render.yaml` blueprint is included at the repo root if you'd rather use Render's **Blueprint** deploy flow (New → Blueprint) instead of the manual steps above — it pre-fills the same settings.\n\n> **Free tier note**: Render's free web services spin down after inactivity, so the first request after idling can take 30–60s to wake up before your report generation even starts.\n\n### 3. Deploy the frontend on Vercel\n\n1. Go to [vercel.com/new](https://vercel.com/new) and import the same GitHub repository.\n2. Configure the project:\n   - **Root Directory**: `frontend`\n   - **Framework Preset**: Vite (auto-detected)\n   - **Build Command**: `npm run build`\n   - **Output Directory**: `dist`\n3. Add an environment variable:\n   - `VITE_API_URL` = `https://<your-backend>.onrender.com` (the Render URL from step 2, no trailing slash)\n4. Click **Deploy**. Vercel gives you a URL, e.g. `https://your-app.vercel.app`.\n\n### 4. Close the loop: update CORS on Render\n\nGo back to the Render service → **Environment** → update `CORS_ORIGINS` to include your Vercel domain(s), then save (Render will redeploy automatically):\n\n```env\nCORS_ORIGINS=https://your-app.vercel.app,http://localhost:5173\n```\n\nInclude any Vercel preview-deployment domains too if you plan to test those (e.g. `https://your-app-git-branch-name.vercel.app`).\n\n### 5. Verify end-to-end\n\nOpen your Vercel URL, submit a product idea, and confirm the request reaches Render (Network tab → calls to `/run` and repeated polling of `/status/{job_id}`) and a report comes back. If it fails, see Troubleshooting below.\n\n---\n\n## 🧪 Troubleshooting\n\n- **\"Could not connect to the server\"** in the UI → make sure the FastAPI backend is running on the port set in `frontend/.env` (`VITE_API_URL`).\n- **CORS errors in the browser console** → make sure `backend/.env`'s `CORS_ORIGINS` includes the exact origin your frontend is served from (default `http://localhost:5173`).\n- **`GroqException` / authentication errors** → double check `GROQ_API_KEY` and `MODEL` in `backend/.env`; make sure the model name is currently supported by Groq (see `https://console.groq.com/docs/models`).\n- **Report generation seems stuck** → this is expected; a full 5-agent run can take several minutes. The frontend polls `/status/{job_id}` every 5 seconds until it completes or fails.\n- **Works locally but fails after deploying** → almost always `CORS_ORIGINS` on Render doesn't yet include your Vercel domain, or `VITE_API_URL` on Vercel is wrong/missing a scheme (`https://`). Check both, then redeploy.\n- **First request after a while is very slow on Render** → the free instance type spins down when idle and needs ~30–60s to cold-start. 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