Update app.py

app.py

@@ -4,37 +4,25 @@ import numpy as np
 import requests
 from fastapi import FastAPI
 from pydantic import BaseModel
-from fastapi.responses import HTMLResponse
-from fastapi.middleware.cors import CORSMiddleware
+import uvicorn
 import insightface
 import gradio as gr
 
-# ---------- Load Face Detector ----------
+# ---------- Load Face Detector + Recognition Model ----------
 model = insightface.app.FaceAnalysis(name="buffalo_l")
 model.prepare(ctx_id=0, det_size=(640, 640))
 
 # ---------- FastAPI App ----------
 app = FastAPI()
 
-# CORS for FlutterFlow
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-
-# ---------- API Request Model ----------
+# ---------- API Request Schema ----------
 class CompareRequest(BaseModel):
-    image1: str | None = None
-    image2: str | None = None
-    image1_url: str | None = None
-    image2_url: str | None = None
-
+    image1: str | None = None     # base64
+    image2: str | None = None     # base64
+    image1_url: str | None = None # URL
+    image2_url: str | None = None # URL
 
-# ---------- Helpers ----------
+# ---------- Helper: Convert base64 to CV2 image ----------
 def b64_to_img(b64_string):
     try:
         img_data = base64.b64decode(b64_string)
@@ -44,6 +32,7 @@ def b64_to_img(b64_string):
     except:
         return None
 
+# ---------- Helper: Convert URL to CV2 image ----------
 def url_to_img(url):
     try:
         resp = requests.get(url, timeout=5)
@@ -53,18 +42,18 @@ def url_to_img(url):
     except:
         return None
 
+# ---------- Helper: Extract face embedding ----------
 def get_embedding(img):
     faces = model.get(img)
     if len(faces) == 0:
         return None
-    return faces[0].embedding
+    return faces[0].embedding  # first detected face
 
-
-# ---------- POST /compare ----------
+# ---------- POST /compare API ----------
 @app.post("/compare")
 async def compare_faces(req: CompareRequest):
 
-    # Load image 1
+    # ---- Load Image 1 ----
     if req.image1:
         img1 = b64_to_img(req.image1)
     elif req.image1_url:
@@ -72,7 +61,7 @@ async def compare_faces(req: CompareRequest):
     else:
         img1 = None
 
-    # Load image 2
+    # ---- Load Image 2 ----
     if req.image2:
         img2 = b64_to_img(req.image2)
     elif req.image2_url:
@@ -87,32 +76,39 @@ async def compare_faces(req: CompareRequest):
     emb2 = get_embedding(img2)
 
     if emb1 is None or emb2 is None:
-        return {"error": "No face detected."}
+        return {"error": "No face detected in one or both images."}
 
-    similarity = np.dot(emb1, emb2) / (
-        np.linalg.norm(emb1) * np.linalg.norm(emb2)
-    )
-    matched = similarity > 0.55
+    # Cosine similarity
+    similarity = np.dot(emb1, emb2) / (np.linalg.norm(emb1) * np.linalg.norm(emb2))
+
+    matched = similarity > 0.55  # threshold
 
     return {
         "similarity": float(similarity),
         "match": matched
     }
 
-
-# ---------- Gradio UI ----------
+# ---------- Gradio UI (TEXT INPUT instead of upload) ----------
 def gradio_ui(img1_text, img2_text):
-    # Detect format automatically
-    def load(s):
-        if s.startswith("http"):
-            return url_to_img(s)
-        return b64_to_img(s)
-
-    img1 = load(img1_text)
-    img2 = load(img2_text)
+    """
+    Accepts:
+      - base64 string
+      - or URL
+    Automatically detects which one.
+    """
+
+    # --- Determine type of input ---
+    def load_any(input_str):
+        if input_str.startswith("http://") or input_str.startswith("https://"):
+            return url_to_img(input_str)
+        else:
+            return b64_to_img(input_str)
+
+    img1 = load_any(img1_text)
+    img2 = load_any(img2_text)
 
     if img1 is None or img2 is None:
-        return "Invalid image."
+        return "Invalid image data or URL."
 
     emb1 = get_embedding(img1)
     emb2 = get_embedding(img2)
@@ -120,25 +116,17 @@ def gradio_ui(img1_text, img2_text):
     if emb1 is None or emb2 is None:
         return "Face not detected."
 
-    similarity = np.dot(emb1, emb2) / (
-        np.linalg.norm(emb1) * np.linalg.norm(emb2)
-    )
-    match = similarity > 0.55
-
-    return f"Similarity: {similarity:.3f} | Match: {match}"
+    similarity = np.dot(emb1, emb2) / (np.linalg.norm(emb1) * np.linalg.norm(emb2))
+    matched = similarity > 0.55
 
+    return f"Similarity: {similarity:.3f} | Match: {matched}"
 
-demo = gr.Interface(
+gr.Interface(
     fn=gradio_ui,
     inputs=[
-        gr.Textbox(label="Image 1 (URL or Base64)"),
-        gr.Textbox(label="Image 2 (URL or Base64)")
+        gr.Textbox(label="Image 1 (base64 or URL)"),
+        gr.Textbox(label="Image 2 (base64 or URL)")
     ],
     outputs="text",
-    title="Face Compare API"
-)
-
-# ---------- Serve Gradio UI at "/" ----------
-@app.get("/", response_class=HTMLResponse)
-async def root():
-    return demo.launch(share=False, inline=True)
+    title="Face Match API (Text Input)"
+).launch()