Upload main.py

app/main.py

@@ -0,0 +1,148 @@
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).resolve().parent.parent))
+#print(sys.path)
+from typing import Any
+
+from fastapi import FastAPI, Request, APIRouter, File, UploadFile
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
+from fastapi.middleware.cors import CORSMiddleware
+from app.config import settings
+from app import __version__
+from app.Hackathon_setup import face_recognition, exp_recognition
+
+import numpy as np
+from PIL import Image
+
+
+app = FastAPI(
+    title=settings.PROJECT_NAME, openapi_url=f"{settings.API_V1_STR}/openapi.json"
+)
+
+# To store files uploaded by users
+app.mount("/static", StaticFiles(directory="app/static"), name="static")
+
+# To access Templates directory
+templates = Jinja2Templates(directory="app/templates")
+
+simi_filename1 = None
+simi_filename2 = None
+face_rec_filename = None
+expr_rec_filename = None
+
+
+####################################  Home Page endpoints  #################################################
+@app.get("/")
+async def root(request: Request):
+    return templates.TemplateResponse("index.html", {'request': request,})
+
+
+####################################  Face Similarity endpoints  #################################################
+@app.get("/similarity/")
+async def similarity_root(request: Request):
+    return templates.TemplateResponse("similarity.html", {'request': request,})
+
+
+@app.post("/predict_similarity/")
+async def create_upload_files(request: Request, file1: UploadFile = File(...), file2: UploadFile = File(...)):
+    global simi_filename1
+    global simi_filename2
+    
+    if 'image' in file1.content_type:
+        contents = await file1.read()
+        simi_filename1 = 'app/static/' + file1.filename
+        with open(simi_filename1, 'wb') as f:
+            f.write(contents)
+    
+    if 'image' in file2.content_type:
+        contents = await file2.read()
+        simi_filename2 = 'app/static/' + file2.filename
+        with open(simi_filename2, 'wb') as f:
+            f.write(contents)
+    
+    img1 = Image.open(simi_filename1)
+    img1 =  np.array(img1).reshape(img1.size[1], img1.size[0], 3).astype(np.uint8)
+    
+    img2 = Image.open(simi_filename2)
+    img2 =  np.array(img2).reshape(img2.size[1], img2.size[0], 3).astype(np.uint8)
+    
+    result = face_recognition.get_similarity(img1, img2)
+    #print(result)
+    
+    return templates.TemplateResponse("predict_similarity.html", {"request": request,
+                                                       "result": np.round(result, 3),
+                                                       "simi_filename1": '../static/'+file1.filename,
+                                                       "simi_filename2": '../static/'+file2.filename,})
+    
+
+####################################  Face Recognition endpoints  #################################################
+@app.get("/face_recognition/")
+async def face_recognition_root(request: Request):
+    return templates.TemplateResponse("face_recognition.html", {'request': request,})
+
+
+@app.post("/predict_face_recognition/")
+async def create_upload_files(request: Request, file3: UploadFile = File(...)):
+    global face_rec_filename
+    
+    if 'image' in file3.content_type:
+        contents = await file3.read()
+        face_rec_filename = 'app/static/' + file3.filename
+        with open(face_rec_filename, 'wb') as f:
+            f.write(contents)
+    
+    img1 = Image.open(face_rec_filename)
+    img1 =  np.array(img1).reshape(img1.size[1], img1.size[0], 3).astype(np.uint8)
+        
+    result = face_recognition.get_face_class(img1)
+    print(result)
+    
+    return templates.TemplateResponse("predict_face_recognition.html", {"request": request,
+                                                       "result": result,
+                                                       "face_rec_filename": '../static/'+file3.filename,})
+
+
+####################################  Expresion Recognition endpoints  #################################################
+@app.get("/expr_recognition/")
+async def expr_recognition_root(request: Request):
+    return templates.TemplateResponse("expr_recognition.html", {'request': request,})
+
+
+@app.post("/predict_expr_recognition/")
+async def create_upload_files(request: Request, file4: UploadFile = File(...)):
+    global expr_rec_filename
+    
+    if 'image' in file4.content_type:
+        contents = await file4.read()
+        expr_rec_filename = 'app/static/' + file4.filename
+        with open(expr_rec_filename, 'wb') as f:
+            f.write(contents)
+    
+    img1 = Image.open(expr_rec_filename)
+    img1 =  np.array(img1).reshape(img1.size[1], img1.size[0], 3).astype(np.uint8)
+        
+    result = exp_recognition.get_expression(img1)
+    print(result)
+    
+    return templates.TemplateResponse("predict_expr_recognition.html", {"request": request,
+                                                       "result": result,
+                                                       "expr_rec_filename": '../static/'+file4.filename,})
+
+
+
+# Set all CORS enabled origins
+if settings.BACKEND_CORS_ORIGINS:
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=[str(origin) for origin in settings.BACKEND_CORS_ORIGINS],
+        allow_credentials=True,
+        allow_methods=["*"],
+        allow_headers=["*"],
+    )
+
+
+# Start app
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8001)