Rename app.py to main.py

main.py

@@ -0,0 +1,86 @@
+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()
+
+API_KEY = "your_api_key_here"
+client = Together(api_key=API_KEY)
+MODEL_NAME = "meta-llama/Llama-Vision-Free"
+
+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))