Update app.py

app.py

@@ -1,169 +1,176 @@
-# Import libraries
-import pandas as pd
-from ultralytics import YOLO
-from flask import Flask, render_template, request, send_from_directory
-from werkzeug.utils import secure_filename
-import os
-import pandas as pd
-import shutil
-
-
-
-def clr_static():
-    """
-    for removing the static folder (for memoery saving in web)
-    """
-    dir = 'static'
-    try:
-        shutil.rmtree(dir)
-        print("Cleared Static")
-    except:
-        pass
-
-def clr_old_upload():
-    """
-    for removing the old images uploaded file (for memoery saving in web)
-    """
-    dir = 'UPLOAD_FOLDER'
-    try:
-        shutil.rmtree(dir)
-        print("Cleared UPLOAD_FOLDER")
-    except:
-        pass
-
-
-#making dir to save the generated csv file
-def mk_csv_folder():
-    try:
-        os.mkdir("static/CSV_File")
-        print("made csv_folder")
-    except:
-        pass
-
-#Creating UPLOAD_FOLDER dir to save the Uploaded file
-def mk_uploaded_folder():
-    try:
-        os.mkdir("UPLOAD_FOLDER")
-        print("made UPLOAD_FOLDER")
-    except:
-        pass
-
-#Creating static dir to save the generated file
-def mk_static_folder():
-    try:
-        os.mkdir("static")
-        print("made static folder")
-    except:
-        pass
-
-
-# creating the model  instance
-model = YOLO('best.pt')
-
-
-def Use_yolo(img_path):
-    """
-    :param img_path:
-    :return: image with bbox , csv file
-    """
-    all_data =[]
-
-    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")
-    # 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
-    confidences = results[0].boxes.conf.tolist()  # Confidence scores
-    names = results[0].names  # Class names dictionary
-
-    if not boxes:
-        # If no detections, add NEG as the class
-        all_data.append({
-            'Class': 'No Object Found',  # Default value (no detection)
-            'x_min': 'Nan',  # Default value (no detection)
-            'y_min': 'Nan',  # Default value (no detection)
-            'x_max': 'Nan',  # Default value (no detection)
-            'y_max': 'Nan'  # Default value (no detection)
-        })
-    else:
-        # Iterate through the results for this image
-        for box, cls, conf in zip(boxes, classes, confidences):
-            x_min, y_min, x_max, y_max = box
-            detected_class = names[int(cls)]  # Get the class name from the names dictionary
-
-            # Add the result to the all_data list
-            all_data.append({
-                'Class': detected_class,
-                'x_min': int(x_min),
-                'y_min': int(y_min),
-                'x_max': int(x_max),
-                'y_max': int(y_max),
-                'Confidence/Probability Score': conf
-            })
-
-    sub = pd.DataFrame(all_data)
-
-    sub.to_csv("static/CSV_File/WBC_File.csv", index=False)
-
-
-app = Flask(__name__)
-app.config['UPLOAD_FOLDER'] = 'UPLOAD_FOLDER'
-
-@app.route('/')
-def home():
-    return render_template('index.html')
-
-@app.route('/index')
-def home_click():
-    return render_template('index.html')
-
-@app.route('/document')
-def document_click():
-    df = pd.read_csv("Documents/results.csv")  # Reading CSV File
-    # Convert DataFrame to a list of dictionaries
-    data = df.to_dict(orient='records')
-
-    return render_template('document.html', data=data, columns=df.columns)
-
-
-@app.route('/wbc_info')
-def wbc_info_click():
-    return render_template('wbc_info.html')
-
-
-@app.route('/upload', methods=['GET', 'POST'])
-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()
-            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
-
-            for image in imageList:
-                ### Applying yolo model for object detection on uploaded files
-                Use_yolo("UPLOAD_FOLDER/"+image)
-
-            pred_image_list = os.listdir("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
-            # Convert DataFrame to a list of dictionaries
-            data = df.to_dict(orient='records')
-            show_csv_heading = True
-            return render_template("index.html", pred_image_list= pred_image_list, data=data, columns=df.columns, show_csv_heading=show_csv_heading)
-
-    except:
-        return render_template("error.html")
-
-if __name__ == "__main__":
-    app.run(host='0.0.0.0', port=7860)
-
+# Import libraries
+import pandas as pd
+from ultralytics import YOLO
+from flask import Flask, render_template, request, send_from_directory
+from werkzeug.utils import secure_filename
+import os
+import pandas as pd
+import shutil
+
+folder_path = "UPLOAD"  # Change this to your desired folder name
+
+# Create the folder if it does not exist
+if not os.path.exists(folder_path):
+    os.makedirs(folder_path)
+    print(f"Folder '{folder_path}' created successfully.")
+else:
+    print(f"Folder '{folder_path}' already exists.")
+
+def clr_static():
+    """
+    for removing the static folder (for memoery saving in web)
+    """
+    dir = 'static'
+    try:
+        shutil.rmtree(dir)
+        print("Cleared Static")
+    except:
+        pass
+
+def clr_old_upload():
+    """
+    for removing the old images uploaded file (for memoery saving in web)
+    """
+    dir = 'UPLOAD_FOLDER'
+    try:
+        shutil.rmtree(dir)
+        print("Cleared UPLOAD_FOLDER")
+    except:
+        pass
+
+
+#making dir to save the generated csv file
+def mk_csv_folder():
+    try:
+        os.mkdir("static/CSV_File")
+        print("made csv_folder")
+    except:
+        pass
+
+#Creating UPLOAD_FOLDER dir to save the Uploaded file
+def mk_uploaded_folder():
+    try:
+        os.mkdir("UPLOAD_FOLDER")
+        print("made UPLOAD_FOLDER")
+    except:
+        pass
+
+#Creating static dir to save the generated file
+def mk_static_folder():
+    try:
+        os.mkdir("static")
+        print("made static folder")
+    except:
+        pass
+
+
+# creating the model  instance
+model = YOLO('best.pt')
+
+
+def Use_yolo(img_path):
+    """
+    :param img_path:
+    :return: image with bbox , csv file
+    """
+    all_data =[]
+
+    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")
+    # 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
+    confidences = results[0].boxes.conf.tolist()  # Confidence scores
+    names = results[0].names  # Class names dictionary
+
+    if not boxes:
+        # If no detections, add NEG as the class
+        all_data.append({
+            'Class': 'No Object Found',  # Default value (no detection)
+            'x_min': 'Nan',  # Default value (no detection)
+            'y_min': 'Nan',  # Default value (no detection)
+            'x_max': 'Nan',  # Default value (no detection)
+            'y_max': 'Nan'  # Default value (no detection)
+        })
+    else:
+        # Iterate through the results for this image
+        for box, cls, conf in zip(boxes, classes, confidences):
+            x_min, y_min, x_max, y_max = box
+            detected_class = names[int(cls)]  # Get the class name from the names dictionary
+
+            # Add the result to the all_data list
+            all_data.append({
+                'Class': detected_class,
+                'x_min': int(x_min),
+                'y_min': int(y_min),
+                'x_max': int(x_max),
+                'y_max': int(y_max),
+                'Confidence/Probability Score': conf
+            })
+
+    sub = pd.DataFrame(all_data)
+
+    sub.to_csv("static/CSV_File/WBC_File.csv", index=False)
+
+
+app = Flask(__name__)
+app.config['UPLOAD_FOLDER'] = 'UPLOAD_FOLDER'
+
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+@app.route('/index')
+def home_click():
+    return render_template('index.html')
+
+@app.route('/document')
+def document_click():
+    df = pd.read_csv("Documents/results.csv")  # Reading CSV File
+    # Convert DataFrame to a list of dictionaries
+    data = df.to_dict(orient='records')
+
+    return render_template('document.html', data=data, columns=df.columns)
+
+
+@app.route('/wbc_info')
+def wbc_info_click():
+    return render_template('wbc_info.html')
+
+
+@app.route('/upload', methods=['GET', 'POST'])
+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()
+            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
+
+            for image in imageList:
+                ### Applying yolo model for object detection on uploaded files
+                Use_yolo("UPLOAD_FOLDER/"+image)
+
+            pred_image_list = os.listdir("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
+            # Convert DataFrame to a list of dictionaries
+            data = df.to_dict(orient='records')
+            show_csv_heading = True
+            return render_template("index.html", pred_image_list= pred_image_list, data=data, columns=df.columns, show_csv_heading=show_csv_heading)
+
+    except:
+        return render_template("error.html")
+
+if __name__ == "__main__":
+    app.run(host='0.0.0.0', port=7860)
+