Python Source code

main.py

+from fastapi import FastAPI, Request
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import HTMLResponse
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
+from tensorflow.keras.models import load_model
+from tensorflow.keras.utils import get_file 
+from tensorflow.keras.utils import load_img 
+from tensorflow.keras.utils import img_to_array
+from tensorflow import expand_dims
+from tensorflow.nn import softmax
+from numpy import argmax
+from numpy import max
+from numpy import array
+from json import dumps
+from uvicorn import run
+import os
+import requests
+import shutil
+
+app = FastAPI()
+
+origins = ["*"]
+methods = ["*"]
+headers = ["*"]
+
+app.add_middleware(
+    CORSMiddleware, 
+    allow_origins = origins,
+    allow_credentials = True,
+    allow_methods = methods,
+    allow_headers = headers    
+)
+
+model_dir = "Fish_identificaton.h5"
+model = load_model(model_dir)
+
+# Providing the folder path
+origin = r"C:\xampp\htdocs\templates\php\files\fishimage.jpg"
+target = r"E:\4th Year\1st Sem\Research\Docker\Backend\fishimage.jpg"
+target2 = r"E:\4th Year\1st Sem\Research\Docker\Backend\templates\fishimage.jpg"
+
+# Function to get result
+
+# async def get_net_image_prediction(image_link: str = ""):
+#     if image_link == "":
+#         print(image_link)
+#         return {"message": "No image link provided"}
+    
+#     response = requests.get(image_link)
+#     filename = "fishimage.jpg"
+
+#     with open(filename, "wb") as f:
+#         f.write(response.content)
+
+#     # img_path = get_file(
+#     #     origin = image_link
+#     # )
+
+def get_net_image_prediction():
+
+    # myfile = open('C:/xampp/htdocs/templates/php/files/fishimage.jpg')
+    # myfile = open('C:\\xampp\\htdocs\\templates\\php\\files\\fishimage.jpg','r')
+
+    shutil.copy(origin,target)
+    shutil.copy(origin,target2)
+    img_path = "fishimage.jpg"
+
+    img = load_img(
+        img_path, 
+        target_size = (331, 331)
+    )
+
+    img_array = img_to_array(img)
+    img_array = expand_dims(img_array, 0)
+
+    pred = model.predict(img_array)
+
+    max_value = max(pred[0])
+    duplicate_list = list(pred[0])
+    index = duplicate_list.index(max_value)
+    lables = ['feed', 'group', 'individual', 'aggresive', 'nervous']
+    rt= lables[index]
+
+    return {
+        rt
+        
+    }
+
+app.mount("/static", StaticFiles(directory="static"), name="static")
+
+
+templates = Jinja2Templates(directory="templates")
+
+@app.get("/")
+async def root():
+    return {"message": "Welcome to the Fish Identifications API!"}
+
+# @app.post("/net/image/prediction/")
+
+# def getFishtype():
+#     rtt = get_net_image_prediction()
+#     return rtt
+
+
+
+@app.get("/getResult")
+async def send_image_get_prediction(request: Request):
+    
+            rtt = get_net_image_prediction()
+
+            return templates.TemplateResponse("Saveimage.html", {"request": request, "variable": rtt})
+
+# async def send_image_get_prediction():
+
+#             rtt = get_net_image_prediction()
+        
+#             return rtt
+
+
+
+if __name__ == "__main__":
+    port = int(os.environ.get('PORT', 5000))
+    run(app, host="0.0.0.0", port=port)
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