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89c6054 a176b28 89c6054 a176b28 89c6054 41f7e4f a176b28 89c6054 a176b28 89c6054 a176b28 89c6054 a176b28 89c6054 a176b28 89c6054 a176b28 89c6054 a176b28 89c6054 a176b28 89c6054 a176b28 89c6054 a176b28 89c6054 a176b28 9b173c6 a176b28 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | from fastapi import FastAPI, UploadFile, File, HTTPException
from fastapi.responses import JSONResponse
from PIL import Image
import tempfile
import os
import base64
import cv2
import io
import re
from together import Together
import releaf_ai # this should still contain your SYSTEM_PROMPT
app = FastAPI()
# Init Together client
API_KEY = "1495bcdf0c72ed1e15d0e3e31e4301bd665cb28f2291bcc388164ed745a7aa24"
client = Together(api_key=API_KEY)
MODEL_NAME = "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8"
SYSTEM_PROMPT = releaf_ai.SYSTEM_PROMPT
def encode_image_to_base64(image: Image.Image) -> str:
buffered = io.BytesIO()
image.save(buffered, format="JPEG")
return base64.b64encode(buffered.getvalue()).decode("utf-8")
def extract_score(text: str):
match = re.search(r"(?i)Score:\s*(\d+)", text)
return int(match.group(1)) if match else None
def extract_activity(text: str):
match = re.search(r"(?i)Detected Activity:\s*(.+?)\n", text)
return match.group(1).strip() if match else "Unknown"
@app.post("/predict")
async def predict(file: UploadFile = File(...)):
try:
if file.content_type.startswith("image"):
image = Image.open(io.BytesIO(await file.read())).convert("RGB")
elif file.content_type.startswith("video"):
temp_path = tempfile.NamedTemporaryFile(delete=False).name
with open(temp_path, "wb") as f:
f.write(await file.read())
cap = cv2.VideoCapture(temp_path)
total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
interval = max(total // 9, 1)
frames = []
for i in range(9):
cap.set(cv2.CAP_PROP_POS_FRAMES, i * interval)
ret, frame = cap.read()
if ret:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
img = Image.fromarray(frame).resize((256, 256))
frames.append(img)
cap.release()
os.remove(temp_path)
w, h = frames[0].size
grid = Image.new("RGB", (3 * w, 3 * h))
for idx, frame in enumerate(frames):
grid.paste(frame, ((idx % 3) * w, (idx // 3) * h))
image = grid
else:
raise HTTPException(status_code=400, detail="Unsupported file type")
b64_img = encode_image_to_base64(image)
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": [
{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{b64_img}"}}
]}
]
res = client.chat.completions.create(model=MODEL_NAME, messages=messages)
reply = res.choices[0].message.content
return JSONResponse({
"points": extract_score(reply),
"task": extract_activity(reply),
"raw": reply
})
except Exception as e:
raise HTTPException(status_code=500, detail=str(e)) |