finalized v1

app.py

@@ -1,341 +1,3 @@
-# import os
-# import io
-# import base64
-# import threading
-# import traceback
-# import gc
-# from typing import Optional
-
-# from flask import Flask, request, jsonify, send_from_directory
-# from PIL import Image
-# import numpy as np
-# import requests
-# import torch
-
-# # Set environment variables for CPU-only operation
-# os.environ.setdefault("MPLCONFIGDIR", "/tmp/.matplotlib")
-# os.environ.setdefault("FONTCONFIG_PATH", "/tmp/.fontconfig")
-# os.environ.setdefault("FONTCONFIG_FILE", "/etc/fonts/fonts.conf")
-# os.environ.setdefault("CUDA_VISIBLE_DEVICES", "")
-# os.environ.setdefault("OMP_NUM_THREADS", "4")
-# os.environ.setdefault("MKL_NUM_THREADS", "4")
-# os.environ.setdefault("OPENBLAS_NUM_THREADS", "4")
-
-# # Create writable fontconfig cache
-# os.makedirs("/tmp/.fontconfig", exist_ok=True)
-# os.makedirs("/tmp/.matplotlib", exist_ok=True)
-
-# # Limit torch threads
-# try:
-#     torch.set_num_threads(4)
-# except Exception:
-#     pass
-
-# import supervision as sv
-# from rfdetr import RFDETRSegPreview
-
-# app = Flask(__name__, static_folder="static", static_url_path="/")
-
-# # Checkpoint URL & local path
-# CHECKPOINT_URL = "https://huggingface.co/Subh775/Segment-Tulsi-TFs/resolve/main/checkpoint_best_total.pth"
-# CHECKPOINT_PATH = os.path.join("/tmp", "checkpoint_best_total.pth")
-
-# MODEL_LOCK = threading.Lock()
-# MODEL = None
-
-
-# def download_file(url: str, dst: str, chunk_size: int = 8192):
-#     """Download file if not exists"""
-#     if os.path.exists(dst) and os.path.getsize(dst) > 0:
-#         print(f"[INFO] Checkpoint already exists at {dst}")
-#         return dst
-#     print(f"[INFO] Downloading weights from {url} -> {dst}")
-#     try:
-#         r = requests.get(url, stream=True, timeout=180)
-#         r.raise_for_status()
-#         with open(dst, "wb") as fh:
-#             for chunk in r.iter_content(chunk_size=chunk_size):
-#                 if chunk:
-#                     fh.write(chunk)
-#         print("[INFO] Download complete.")
-#         return dst
-#     except Exception as e:
-#         print(f"[ERROR] Download failed: {e}")
-#         raise
-
-
-# def init_model():
-#     """Lazily initialize the RF-DETR model and cache it in global MODEL."""
-#     global MODEL
-#     with MODEL_LOCK:
-#         if MODEL is not None:
-#             print("[INFO] Model already loaded, returning cached instance")
-#             return MODEL
-#         try:
-#             # Ensure checkpoint present
-#             if not os.path.exists(CHECKPOINT_PATH):
-#                 print("[INFO] Checkpoint not found, downloading...")
-#                 download_file(CHECKPOINT_URL, CHECKPOINT_PATH)
-#             else:
-#                 print(f"[INFO] Using existing checkpoint at {CHECKPOINT_PATH}")
-
-#             print("[INFO] Loading RF-DETR model (CPU mode)...")
-#             MODEL = RFDETRSegPreview(pretrain_weights=CHECKPOINT_PATH)
-            
-#             # Try to optimize for inference
-#             try:
-#                 print("[INFO] Optimizing model for inference...")
-#                 MODEL.optimize_for_inference()
-#                 print("[INFO] Model optimization complete")
-#             except Exception as e:
-#                 print(f"[WARN] optimize_for_inference() skipped/failed: {e}")
-            
-#             print("[INFO] Model ready for inference")
-#             return MODEL
-#         except Exception as e:
-#             print(f"[ERROR] Model initialization failed: {e}")
-#             traceback.print_exc()
-#             raise
-
-
-# def decode_data_url(data_url: str) -> Image.Image:
-#     """Decode data URL to PIL Image"""
-#     if data_url.startswith("data:"):
-#         _, b64 = data_url.split(",", 1)
-#         data = base64.b64decode(b64)
-#     else:
-#         try:
-#             data = base64.b64decode(data_url)
-#         except Exception:
-#             raise ValueError("Invalid image data")
-#     return Image.open(io.BytesIO(data)).convert("RGB")
-
-
-# def encode_pil_to_dataurl(pil_img: Image.Image, fmt="PNG") -> str:
-#     """Encode PIL Image to data URL"""
-#     buf = io.BytesIO()
-#     pil_img.save(buf, format=fmt, optimize=False)
-#     buf.seek(0)
-#     return "data:image/{};base64,".format(fmt.lower()) + base64.b64encode(buf.getvalue()).decode("ascii")
-
-
-# def annotate_segmentation(image: Image.Image, detections: sv.Detections) -> Image.Image:
-#     """
-#     Annotate image with segmentation masks using supervision library.
-#     This matches the visualization from rfdetr_seg_infer.py script.
-#     """
-#     try:
-#         # Define color palette
-#         palette = sv.ColorPalette.from_hex([
-#             "#ffff00", "#ff9b00", "#ff8080", "#ff66b2", "#ff66ff", "#b266ff",
-#             "#9999ff", "#3399ff", "#66ffff", "#33ff99", "#66ff66", "#99ff00",
-#         ])
-
-#         # Calculate optimal text scale based on image resolution
-#         text_scale = sv.calculate_optimal_text_scale(resolution_wh=image.size)
-        
-#         print(f"[INFO] Creating annotators with text_scale={text_scale}")
-        
-#         # Create annotators
-#         mask_annotator = sv.MaskAnnotator(color=palette)
-#         polygon_annotator = sv.PolygonAnnotator(color=sv.Color.WHITE)
-#         label_annotator = sv.LabelAnnotator(
-#             color=palette,
-#             text_color=sv.Color.BLACK,
-#             text_scale=text_scale,
-#             text_position=sv.Position.CENTER_OF_MASS
-#         )
-
-#         # Create labels with confidence scores
-#         labels = [
-#             f"Tulsi {float(conf):.2f}"
-#             for conf in detections.confidence
-#         ]
-        
-#         print(f"[INFO] Annotating {len(labels)} detections")
-
-#         # Apply annotations step by step
-#         out = image.copy()
-#         print("[INFO] Applying mask annotation...")
-#         out = mask_annotator.annotate(out, detections)
-#         print("[INFO] Applying polygon annotation...")
-#         out = polygon_annotator.annotate(out, detections)
-#         print("[INFO] Applying label annotation...")
-#         out = label_annotator.annotate(out, detections, labels)
-        
-#         print("[INFO] Annotation complete")
-#         return out
-        
-#     except Exception as e:
-#         print(f"[ERROR] Annotation failed: {e}")
-#         traceback.print_exc()
-#         # Return original image if annotation fails
-#         return image
-
-
-# @app.route("/", methods=["GET"])
-# def index():
-#     """Serve the static UI"""
-#     index_path = os.path.join(app.static_folder or "static", "index.html")
-#     if os.path.exists(index_path):
-#         return send_from_directory(app.static_folder, "index.html")
-#     return jsonify({"message": "RF-DETR Segmentation API is running.", "status": "ready"})
-
-
-# @app.route("/health", methods=["GET"])
-# def health():
-#     """Health check endpoint"""
-#     model_loaded = MODEL is not None
-#     return jsonify({
-#         "status": "healthy",
-#         "model_loaded": model_loaded,
-#         "checkpoint_exists": os.path.exists(CHECKPOINT_PATH)
-#     })
-
-
-# @app.route("/predict", methods=["POST"])
-# def predict():
-#     """
-#     Accepts:
-#       - multipart/form-data with file field "file"
-#       - or JSON {"image": "<data:url...>", "conf": 0.05}
-#     Returns JSON:
-#       {"annotated": "<data:image/png;base64,...>", "confidences": [..], "count": N}
-#     """
-#     print("\n[INFO] ========== New prediction request ==========")
-    
-#     try:
-#         print("[INFO] Initializing model...")
-#         model = init_model()
-#         print("[INFO] Model ready")
-#     except Exception as e:
-#         error_msg = f"Model initialization failed: {e}"
-#         print(f"[ERROR] {error_msg}")
-#         return jsonify({"error": error_msg}), 500
-
-#     # Parse input
-#     img: Optional[Image.Image] = None
-#     conf_threshold = 0.05
-
-#     # Check if file uploaded
-#     if "file" in request.files:
-#         file = request.files["file"]
-#         print(f"[INFO] Processing uploaded file: {file.filename}")
-#         try:
-#             img = Image.open(file.stream).convert("RGB")
-#         except Exception as e:
-#             error_msg = f"Invalid uploaded image: {e}"
-#             print(f"[ERROR] {error_msg}")
-#             return jsonify({"error": error_msg}), 400
-#         conf_threshold = float(request.form.get("conf", conf_threshold))
-#     else:
-#         # Try JSON payload
-#         payload = request.get_json(silent=True)
-#         if not payload or "image" not in payload:
-#             return jsonify({"error": "No image provided. Upload 'file' or JSON with 'image' data-url."}), 400
-#         try:
-#             print("[INFO] Decoding image from data URL...")
-#             img = decode_data_url(payload["image"])
-#         except Exception as e:
-#             error_msg = f"Invalid image data: {e}"
-#             print(f"[ERROR] {error_msg}")
-#             return jsonify({"error": error_msg}), 400
-#         conf_threshold = float(payload.get("conf", conf_threshold))
-
-#     print(f"[INFO] Image size: {img.size}, Confidence threshold: {conf_threshold}")
-
-#     # Optionally downscale large images to reduce memory usage
-#     MAX_SIZE = 1024
-#     if max(img.size) > MAX_SIZE:
-#         w, h = img.size
-#         scale = MAX_SIZE / float(max(w, h))
-#         new_w, new_h = int(round(w * scale)), int(round(h * scale))
-#         print(f"[INFO] Resizing image from {w}x{h} to {new_w}x{new_h}")
-#         img = img.resize((new_w, new_h), Image.Resampling.LANCZOS)
-
-#     # Run inference with no_grad for memory efficiency
-#     try:
-#         print("[INFO] Running inference...")
-#         with torch.no_grad():
-#             detections = model.predict(img, threshold=conf_threshold)
-        
-#         print(f"[INFO] Raw detections: {len(detections)} objects")
-        
-#         # Check if detections exist
-#         if len(detections) == 0 or not hasattr(detections, 'confidence') or len(detections.confidence) == 0:
-#             print("[INFO] No detections above threshold")
-#             # Return original image
-#             data_url = encode_pil_to_dataurl(img, fmt="PNG")
-#             return jsonify({
-#                 "annotated": data_url,
-#                 "confidences": [],
-#                 "count": 0
-#             })
-        
-#         print(f"[INFO] Detections have {len(detections.confidence)} confidence scores")
-#         print(f"[INFO] Confidence range: {min(detections.confidence):.3f} - {max(detections.confidence):.3f}")
-        
-#         # Check if masks exist
-#         if hasattr(detections, 'masks') and detections.masks is not None:
-#             print(f"[INFO] Masks present: shape={np.array(detections.masks).shape if hasattr(detections.masks, '__len__') else 'unknown'}")
-#         else:
-#             print("[WARN] No masks found in detections!")
-        
-#         # Annotate image using supervision library
-#         print("[INFO] Starting annotation...")
-#         annotated_pil = annotate_segmentation(img, detections)
-        
-#         # Extract confidence scores
-#         confidences = [float(conf) for conf in detections.confidence]
-#         print(f"[INFO] Final confidences: {confidences}")
-        
-#         # Encode to data URL
-#         print("[INFO] Encoding annotated image...")
-#         data_url = encode_pil_to_dataurl(annotated_pil, fmt="PNG")
-        
-#         # Clean up
-#         del detections
-#         gc.collect()
-        
-#         print(f"[INFO] ========== Prediction complete: {len(confidences)} leaves detected ==========\n")
-        
-#         return jsonify({
-#             "annotated": data_url,
-#             "confidences": confidences,
-#             "count": len(confidences)
-#         })
-        
-#     except Exception as e:
-#         error_msg = f"Inference failed: {e}"
-#         print(f"[ERROR] {error_msg}")
-#         traceback.print_exc()
-#         return jsonify({"error": error_msg}), 500
-
-
-# if __name__ == "__main__":
-#     print("\n" + "="*60)
-#     print("Starting Tulsi Leaf Segmentation Server")
-#     print("="*60 + "\n")
-    
-#     # Warm model in background thread
-#     def warm():
-#         try:
-#             print("[INFO] Starting model warmup in background...")
-#             init_model()
-#             print("[INFO] ✓ Model warmup complete - ready for predictions")
-#         except Exception as e:
-#             print(f"[ERROR] ✗ Model warmup failed: {e}")
-#             traceback.print_exc()
-    
-#     threading.Thread(target=warm, daemon=True).start()
-    
-#     # Run Flask app
-#     app.run(
-#         host="0.0.0.0",
-#         port=int(os.environ.get("PORT", 7860)),
-#         debug=False
-#     )
-
 import os
 import io
 import base64