Update app.py

app.py

@@ -7,17 +7,15 @@ import os
 import pandas as pd
 import shutil
 
-os.mkdir("/tmp/nice")
-shutil.rmtree("/tmp/nice")
 
-def clr_static():
+def clr_pred():
     """
     for removing the static folder (for memoery saving in web)
     """
     dir = '/tmp/static'
     try:
         shutil.rmtree(dir)
-        print("Cleared Static")
+        print("Cleared Previous Predictions")
     except:
         pass
 
@@ -27,8 +25,8 @@ def clr_old_upload():
     """
     dir = '/tmp/UPLOAD_FOLDER'
     try:
-        shutil.rmtree(dir)
-        print("Cleared UPLOAD_FOLDER")
+      shutil.rmtree(dir)
+      print("Cleared UPLOAD_FOLDER")
     except:
         pass
 
@@ -43,14 +41,14 @@ def mk_csv_folder():
 
 #Creating UPLOAD_FOLDER dir to save the Uploaded file
 def mk_uploaded_folder():
-    try:
-        os.mkdir("/tmp/UPLOAD_FOLDER")
-        print("made UPLOAD_FOLDER")
-    except:
-        pass
+    # try:
+    os.mkdir("/tmp/UPLOAD_FOLDER")
+    print("made UPLOAD_FOLDER")
+    # except:
+    #     pass
 
 #Creating static dir to save the generated file
-def mk_static_folder():
+def mk_pred_folder():
     try:
         os.mkdir("/tmp/static")
         print("made static folder")
@@ -71,7 +69,7 @@ def Use_yolo(img_path):
 
     results = model(img_path, conf=0.1, verbose=False)
     model.predict(img_path, save=True, conf=0.2, show_labels=True,
-                  project='static', name="Image_Prediction")
+                  project='/tmp/static', name="Image_Prediction")
     # Extract bounding boxes, confidence scores, and class labels
     boxes = results[0].boxes.xyxy.tolist()  # Bounding boxes in xyxy format
     classes = results[0].boxes.cls.tolist()  # Class indices
@@ -137,26 +135,25 @@ def wbc_info_click():
 def upload():
     try:
         if request.method == 'POST':
-
             ## clearing old files and folders and creating Folders for saving file
             clr_old_upload()
-            clr_static()
-            mk_static_folder()
+            clr_pred()
+            mk_pred_folder()
             mk_uploaded_folder()
             mk_csv_folder()
             show_csv_heading = False  ## This is set so that co-ordinates table heading will only whow when it is true
             f = request.files['fileInput'] ## geting path of input file
 
             f.save(os.path.join(app.config["UPLOAD_FOLDER"], secure_filename(f.filename) )) ## saving the input image file in UPLOAD_FOLDER
-            imageList = os.listdir("UPLOAD_FOLDER")  # geting listv of image files in UPLOAD_FOLDER
+            imageList = os.listdir("/tmp/UPLOAD_FOLDER")  # geting listv of image files in UPLOAD_FOLDER
 
             for image in imageList:
                 ### Applying yolo model for object detection on uploaded files
-                Use_yolo("UPLOAD_FOLDER/"+image)
+                Use_yolo("/tmp/UPLOAD_FOLDER/"+image)
 
-            pred_image_list = os.listdir("static/Image_Prediction") ## geting the file path of generated image having object detection
+            pred_image_list = os.listdir("/tmp/static/Image_Prediction") ## geting the file path of generated image having object detection
 
-            df = pd.read_csv("static/CSV_File/WBC_File.csv")  # Reading CSV File
+            df = pd.read_csv("/tmp/static/CSV_File/WBC_File.csv")  # Reading CSV File
             # Convert DataFrame to a list of dictionaries
             data = df.to_dict(orient='records')
             show_csv_heading = True
@@ -166,5 +163,4 @@ def upload():
         return render_template("error.html")
 
 if __name__ == "__main__":
-    app.run(host='0.0.0.0', port=7860)
-
+    app.run(debug=True)