integrated with frontend and springboot

app.py

@@ -1,26 +1,39 @@
-from fastapi import FastAPI
-from pydantic import BaseModel
+import tempfile
+from fastapi import FastAPI, File, UploadFile
 from gradio_client import Client, handle_file
 
 app = FastAPI()
+client = Client("kevansoon/FaceRecognition-LivenessDetection-Demo")
 
-client = Client("MiniAiLive/FaceRecognition-LivenessDetection-Demo")
+@app.post("/face-recognition")
+async def face_recognition_post(image: UploadFile = File(...)):
+    # Save uploaded image to temp file
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_file:
+        content = await image.read()
+        temp_file.write(content)
+        temp_filepath = temp_file.name
 
-@app.get("/face-recognition")
-async def face_recognition_get():
-    image1 = "https://qvnhhditkzzeudppuezf.supabase.co/storage/v1/object/public/post-images/post-images/1752289670997-kevan.jpg"
-    image2 = "https://qvnhhditkzzeudppuezf.supabase.co/storage/v1/object/public/post-images/post-images/1752289670997-kevan.jpg"
+    # Hardcoded second image URL
+    image2_url = "https://qvnhhditkzzeudppuezf.supabase.co/storage/v1/object/public/post-images/post-images/1752289670997-kevan.jpg"
+
+    # Call Gradio client with temp file path and URL wrapped in handle_file
     result = client.predict(
-            frame1=handle_file(image1),
-            frame2=handle_file(image2),
-            api_name="/face_compare"
+        frame1=handle_file(temp_filepath),  # Pass local file path wrapped with handle_file
+        frame2=handle_file(image2_url),    # Pass URL wrapped with handle_file
+        api_name="/face_compare"
     )
 
-    str_ = result  # Access first element in array
-    start_index = str_.find("Similarity")  # Find start index of "Similarity", -1 if not found
+    # Optional: remove temp file after use if desired
+    # import os
+    # os.unlink(temp_filepath)
+
+    # Parse the result string as before
+    str_ = result
+    start_index = str_.find("Similarity")
     sliced_str = str_[start_index:] if start_index != -1 else ""
     import re
     decimal_match = re.search(r"<td>(0?\.\d+)<\/td>", sliced_str)
     decimal_number = float(decimal_match.group(1)) if decimal_match else None
     print("Similarity decimal:", decimal_number)
-    return {"result": "Confidence level: " + str(decimal_number)}
+
+    return {"result":str(decimal_number)}