Update api_server.py

api_server.py

@@ -13,8 +13,8 @@ from tensorflow import keras
 from flask import Flask, jsonify, request, render_template, send_file
 import torch
 from collections import Counter
-from clip_model import ClipModel
 import psutil
+from gradio_client import Client, handle_file
 
 # Disable tensorflow warnings
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
@@ -24,6 +24,7 @@ load_type = 'local'
 MODEL_YOLO = "yolo11_detect_best_241024_1.pt"
 MODEL_DIR = "./artifacts/models"
 YOLO_DIR = "./artifacts/yolo"
+GRADIO_URL = "https://50094cfbc694a82dea.gradio.live/"
 #REPO_ID = "1vash/mnist_demo_model"
 
 # Load the saved YOLO model into memory
@@ -97,8 +98,8 @@ check_memory_usage()
 
 # Initialize the Flask application
 app = Flask(__name__)
-# Initialize the ClipModel at the start
-clip_model = ClipModel()
+# # Initialize the ClipModel at the start
+# clip_model = ClipModel()
 
 
 
@@ -168,7 +169,13 @@ def predict():
             
             for yolo_img in yolo_file: # 每張切圖yolo_img
                 print("***** 4. START CLIP *****")
-                top_k_words.append(clip_model.clip_result(yolo_img)) # CLIP預測3個結果(top_k_words)
+                client = Client(GRADIO_URL)
+                clip_result = client.predict(
+                    image=handle_file(yolo_img),
+                    top_k=3,
+                    api_name="/predict"
+                )
+                top_k_words.append(clip_result) # CLIP預測3個結果(top_k_words)
                 #encoded_images.append(image_to_base64(yolo_img))
                 print(f"===== CLIP result:{top_k_words} =====\n")