Build and deploy full-stack profile app

.dockerignore

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+.git
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+*.db
+*.sqlite3
+.ipynb_checkpoints
+BehaviorAnalysis/
+*.mp4
+*.avi
+*.mkv
+*.mov
+sam2_final.ipynb
+resources/example.jpeg
+

Dockerfile

@@ -0,0 +1,19 @@
+FROM python:3.11-slim
+
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    ffmpeg \
+ && rm -rf /var/lib/apt/lists/*
+
+COPY requirements-web.txt ./
+RUN pip install --no-cache-dir -r requirements-web.txt
+
+COPY app ./app
+COPY resources ./resources
+
+EXPOSE 8000
+CMD ["sh", "-c", "uvicorn app.main:app --host 0.0.0.0 --port ${PORT:-8000}"]
+

README.md

@@ -242,3 +242,44 @@ AnimalCare is released under the [MIT License](https://opensource.org/licenses/M
 
 
 ---
+
+## Quickstart (Web App)
+
+Local dev (Python 3.11+):
+
+```sh
+pip install -r requirements-web.txt
+export OPENAI_API_KEY=your_key_here
+uvicorn app.main:app --host 0.0.0.0 --port 8000
+```
+
+Open http://localhost:8000 and upload a short animal video.
+
+API:
+- POST `/api/analyze` with `multipart/form-data` field `video`.
+
+## Deployment
+
+### Docker
+```sh
+docker build -t animalcare .
+docker run -e OPENAI_API_KEY=your_key -p 8000:8000 animalcare
+```
+
+### Render (Docker)
+1. Push this repo to GitHub
+2. Create a Web Service on Render using this repo (Render will read `render.yaml`)
+3. Add environment variable `OPENAI_API_KEY`
+4. Deploy and visit the service URL
+
+### Vercel (Next.js)
+
+- App lives in `web/` (Next.js 14, Tailwind, Framer Motion, Edge API).
+- Import the repo in Vercel and set Root Directory to `web`.
+- Add env var `OPENAI_API_KEY`.
+
+Deploy button:
+
+```
+https://vercel.com/new/clone?repository-url=<YOUR_REPO_URL>&root-directory=web&env=OPENAI_API_KEY
+```

app/__init__.py

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+# Makes app a package

app/main.py

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+import os
+import uvicorn
+from fastapi import FastAPI, UploadFile, File, HTTPException
+from fastapi.responses import HTMLResponse, JSONResponse
+from fastapi.middleware.cors import CORSMiddleware
+from starlette.staticfiles import StaticFiles
+from starlette.responses import RedirectResponse
+
+from .services.analyze import analyze_video_and_summarize
+
+
+app = FastAPI(title="AnimalCare API", version="0.1.0")
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+static_dir = os.path.join(os.path.dirname(__file__), "static")
+os.makedirs(static_dir, exist_ok=True)
+app.mount("/static", StaticFiles(directory=static_dir), name="static")
+
+
+@app.get("/", response_class=HTMLResponse)
+async def index():
+    return RedirectResponse(url="/static/index.html")
+
+
+@app.post("/api/analyze")
+async def analyze(video: UploadFile = File(...)):
+    if not video.filename:
+        raise HTTPException(status_code=400, detail="No file uploaded")
+    suffix = os.path.splitext(video.filename)[1].lower()
+    if suffix not in [".mp4", ".mov", ".avi", ".mkv"]:
+        raise HTTPException(status_code=400, detail="Unsupported file type")
+
+    temp_dir = os.path.join("/tmp", "animalcare")
+    os.makedirs(temp_dir, exist_ok=True)
+    temp_path = os.path.join(temp_dir, video.filename)
+    with open(temp_path, "wb") as f:
+        f.write(await video.read())
+
+    try:
+        result = await analyze_video_and_summarize(temp_path)
+    except Exception as exc:
+        raise HTTPException(status_code=500, detail=str(exc))
+    finally:
+        try:
+            os.remove(temp_path)
+        except Exception:
+            pass
+
+    return JSONResponse(result)
+
+
+def start():
+    port = int(os.environ.get("PORT", "8000"))
+    uvicorn.run("app.main:app", host="0.0.0.0", port=port, reload=False)
+
+
+if __name__ == "__main__":
+    start()
+

app/services/analyze.py

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+import os
+import cv2
+import base64
+from typing import Dict, List
+import numpy as np
+import asyncio
+from openai import OpenAI
+
+
+def _extract_sampled_frames_as_base64(video_path: str, sample_every_n_frames: int = 50, max_frames: int = 30) -> List[str]:
+    capture = cv2.VideoCapture(video_path)
+    if not capture.isOpened():
+        raise RuntimeError("Failed to open video")
+
+    base64_frames: List[str] = []
+    frame_index = 0
+    while True:
+        success, frame = capture.read()
+        if not success:
+            break
+        if frame_index % sample_every_n_frames == 0:
+            success_jpg, buffer = cv2.imencode(".jpg", frame)
+            if success_jpg:
+                base64_frames.append(base64.b64encode(buffer).decode("utf-8"))
+            if len(base64_frames) >= max_frames:
+                break
+        frame_index += 1
+    capture.release()
+    return base64_frames
+
+
+async def analyze_video_and_summarize(video_path: str) -> Dict[str, str]:
+    # Extract representative frames
+    frames = await asyncio.to_thread(_extract_sampled_frames_as_base64, video_path)
+
+    if len(frames) == 0:
+        raise RuntimeError("No frames extracted from video")
+
+    # Prepare prompt
+    prompt = {
+        "role": "user",
+        "content": [
+            "You are an animal behaviorist observing the animal in the following frames. Provide: 1) Activity observations, 2) Signs of illness or discomfort, 3) Health assessment (movement, social interaction, physical form, coat/skin), 4) Recommendations for care, 5) Write as a cohesive narrative suited for a zoo care report. Do not mention frames or numbers.",
+            *map(lambda x: {"image": x, "resize": 768}, frames),
+        ],
+    }
+
+    client = OpenAI()
+    result = await asyncio.to_thread(
+        client.chat.completions.create,
+        model="gpt-4o",
+        messages=[prompt],
+        max_tokens=1200,
+    )
+
+    report = result.choices[0].message.content
+    return {"report": report}
+