diff --git a/api/config/models.py b/api/config/models.py
index 5a0e078..25ca5c9 100644
--- a/api/config/models.py
+++ b/api/config/models.py
@@ -634,3 +634,117 @@ class TaskQueue(BaseModel):
 #
 # def ErrorResponseModel(error, code, message):
 #     return {"error": error, "code": code, "message": message}
+
+
+class FaceSpecBQAT(str, Enum):
+    ipd = "Inter-pupillary distance."
+    confidence = "Confidence level of face dectection (not quality score)."
+    bbox_left = "Left border of the face bounding box coordinates in pixels."
+    bbox_right = "Right border of the face bounding box coordinates in pixels."
+    bbox_upper = "Upper border of the face bounding box coordinates in pixels."
+    bbox_bottom = "Bottom border of the face bounding box coordinates in pixels."
+    eye_closed_left = "Left eye close or not."
+    eye_closed_right = "Right eye close or not."
+    pupil_right_x = "X coordinates of right pupil in pixels."
+    pupil_right_y = "Y coordinates of right pupil in pixels."
+    pupil_left_x = "X coordinates of left pupil in pixels."
+    pupil_left_y = "Y coordinates of left pupil in pixels."
+    yaw_pose = "Yaw in head pose direction."
+    yaw_degree = "Yaw in head pose degree."
+    pitch_pose = "Pitch in head pose direction."
+    pitch_degree = "Pitch in head pose degree."
+    roll_pose = "Roll in head pose direction."
+    roll_degree = "Roll in head pose degree."
+    smile = "Smile detected or not."
+    glassed = "Glasses detected or not."
+
+
+class FaceSpecOFIQ(str, Enum):
+    quality = "MagFace-based unified quality score measure."
+    background_uniformity = "Gradient-based background uniformity."
+    illumination_uniformity = "Illumination unformity by summing up the minima of the histograms of the left and the right side of the face."
+    luminance_mean = "Luminance mean measure computed from the luminance histogram."
+    luminance_variance = (
+        "Luminance variance measure computed from the luminance histogram."
+    )
+    under_exposure_prevention = "Under-exposure prevention by computing the proportion of low-intensity pixels in the luminance image to assess the abscence of under-exposure."
+    over_exposure_prevention = "Over-exposure prevention by computing the proportion of high-intensity pixels in the luminance image to assess the abscence of over-exposure."
+    dynamic_range = "Dynamic range computed from the luminance histogram."
+    sharpness = "Sharpness assessment based on a random forest classifier trained by the OFIQ development team."
+    compression_artifacts = "Assessment of the absence of compression artifact (both JPEG and JPEG2000) based on a CNN trained by the OFIQ development team."
+    natural_colour = "Assessment of the naturalness of the colour based on the conversion of the RGB presentation of the image to the CIELAB colour space."
+    single_face_present = "Assessment of the uniqueness of the most dominant face detected by comparing its size with the size of the second largest face detected."
+    eyes_open = "Eyes openness assessment based on computing eyes aspect ratio from eye landmarks."
+    mouth_closed = (
+        "Mouth closed assessment based on computing a ratio from mouth landmarks."
+    )
+    eyes_visible = "Eyes visibility assessment by measuring the coverage of the eye visibility zone with the result of face occlusion segmentation computed during pre-processing."
+    mouth_occlusion_prevention = "Assessment of the absence of mouth occlusion by measuring the coverage of the mouth region from mouth landmarks with the result of face occlusion segmentation computed on pre-processing."
+    face_occlusion_prevention = "Assessment of the absence of face occlusion by measuring the coverage of the landmarked region with the result of face occlusion segmentation computed during pre-processing."
+    inter_eye_distance = " 	Inter-eye distance assessment based on computing the Euclidean length of eyes’ centres and multiplication with the secant of the yaw angle computed during pre-processing."
+    head_size = "Size of the head based on computing the height of the face computed from facial landmarks with the height of the image."
+    leftward_crop_of_the_face_image = "Left of the face image crop."
+    rightward_crop_of_the_face_image = "Right of the face image crop."
+    downward_crop_of_the_face_image = "Bottom of the face image crop."
+    upward_crop_of_the_face_image = "Top of the face image crop."
+    head_pose_yaw = "Pose angle yaw frontal alignment based on the 3DDFAV2."
+    head_pose_pitch = "Pose angle pitch frontal alignment based on the 3DDFAV2."
+    head_pose_roll = "Pose angle roll frontal alignment based on the 3DDFAV2."
+    expression_neutrality = "Expression neutrality estimation based on a fusion of HSEMotion with Efficient-Expression-Neutrality-Estimation."
+    no_head_coverings = "Assessment of the absence of head coverings by counting the pixels being labeled as head covers in the mask output by the face parsing computed during pre-processing."
+
+
+class FaceSpecBIQT(str, Enum):
+    background_deviation = "Image background deviation."
+    background_grayness = "Image background grayness."
+    blur = "Overall image blurriness."
+    blur_face = "Face area blurriness."
+    focus = "Overall image focus."
+    focus_face = "Face area focus."
+    openbr_confidence = "Confidence value from openbr."
+    opencv_IPD = "Inter eye distance from opencv."
+    over_exposure = "Overall image exposure value."
+    over_exposure_face = "Face area exposure value."
+    quality = "Overall quality score."
+    skin_ratio_face = "Skin to face area ratio."
+    skin_ratio_full = "Skin to fill image area ratio."
+
+
+class FingerprintSpecDefault(str, Enum):
+    NFIQ2 = "NIST/NFIQ2 quality score."
+    UniformImage = "Standard deviation of gray levels in image indicates uniformity."
+    EmptyImageOrContrastTooLow = "The image is blank or the contrast is too low."
+    FingerprintImageWithMinutiae = "Number of minutia in image."
+    SufficientFingerprintForeground = "Number of pixels in the computed foreground."
+    EdgeStd = "Metric to identify malformed images."
+
+
+class IrisSpecDefault(str, Enum):
+    quality = "An overall quality score that leverages several statistics together."
+    contrast = "Raw score quantifying overall image contrast."
+    sharpness = "Raw score quantifying the sharpness of the image."
+    iris_diameter = "Raw diameter of the iris measured in pixels."
+    percent_visible_iris = "Percentage of visible iris area."
+    iris_pupil_gs = "Raw measure quantifying how distinguishable the boundary is between the pupil and the iris."
+    iris_sclera_gs = "Raw measure quantifying how distinguishable the boundary is between the iris and the sclera."
+    iso_overall_quality = "The overall ISO quality score based on the product of normalized individual iso metrics."
+    iso_greyscale_utilization = "The spread of intensity values regarding the pixel values within the iris portion of the image, recommended value: 6 or greater."
+    iso_iris_pupil_concentricity = "The degree to which the pupil centre and the iris centre are in the same location, recommended value: 90 or greater."
+    iso_iris_pupil_contrast = "The image characteristics at the boundary between the iris region and the pupil, recommended value: 30 or greater."
+    iso_iris_pupil_ratio = "The degree to which the pupil is dilated or constricted, recommended value: between 20 and 70."
+    iso_iris_sclera_contrast = "The image characteristics at the boundary between the iris region and the sclera, recommended value: greater than 5."
+    iso_margin_adequacy = "The degree to which the iris portion of the image is centred relative to the edges of the entire image, recommended value: greater than 80."
+    iso_pupil_boundary_circularity = "The circularity of the iris-pupil boundary."
+    iso_sharpness = "The degree of focus present in the image."
+    iso_usable_iris_area = "The fraction of the iris portion of the image that is not occluded by eyelids, eyelashes, or specular reflections."
+
+
+class SpeechSpecDefault(str, Enum):
+    Quality = "Overall quality estimation of the speech audio file."
+    Noisiness = "Quality degradation such as background, circuit, or coding noise."
+    Discontinuity = "Quality degradation caused by isolated or non-stationary distortions, e.g. introduced by packet-loss or clipping."
+    Coloration = "Quality degradation caused by frequency response distortions, e.g. introduced by bandwidth limitation, low bitrate codecs, or packet-loss concealment."
+    Naturalness = "Estimation of the naturalness of synthetic speech."
+    Loudness = (
+        "Influence of the loudness on the perceived quality of transmitted speech."
+    )
\ No newline at end of file
diff --git a/api/utils.py b/api/utils.py
index 0456a21..1b2fdb5 100644
--- a/api/utils.py
+++ b/api/utils.py
@@ -37,6 +37,12 @@
 from api.config import Settings
 from api.config.models import (
     CollectionLog,
+    FaceSpecBIQT,
+    FaceSpecBQAT,
+    FaceSpecOFIQ,
+    FingerprintSpecDefault,
+    IrisSpecDefault,
+    SpeechSpecDefault,
     # DetectorOptions,
     # ReportLog,
     Status,
@@ -633,7 +639,16 @@ async def run_report_tasks(
 
         print(f">> Generate report: {dataset_id}")
 
-        report = [report_task.remote(data, task.get("options"))]
+        options = task.get("options")
+        dataset_log = await log["datasets"].find_one({"collection": dataset_id})
+        options.update(
+            {
+                "mode": dataset_log["options"].get("mode"),
+                "engine": dataset_log["options"].get("engine"),
+            }
+        )
+
+        report = [report_task.remote(data, options)]
         await log["reports"].find_one_and_update(
             {"tid": task["tid"]},
             {
@@ -1054,17 +1069,23 @@ def check_options(options, modality):
 
 
 def generate_report(data, **options):
-    excluded_columns = ["file", "tag", "log"]
     temp = "report.html"
     df = pd.DataFrame.from_dict(data)
+
+    excluded_columns = ["file", "tag", "log"]
+    excluded_columns = [col for col in excluded_columns if col in df.columns]
+
+    df = df.drop(columns=excluded_columns)
+    df = df.loc[:, ~df.columns.str.endswith("_scalar")]
+
     # Ensure numeric columns are not categorized
-    df = df.apply(lambda col: pd.to_numeric(col, errors="ignore"))
-    numeric_columns = df.select_dtypes(include='number').columns
-    # print(f'----------------{numeric_columns}')
+    try:
+        df = df.apply(lambda col: pd.to_numeric(col))
+    except Exception as e:
+        print(f"Error converting string columns to float: {e}")
+    numeric_columns = df.select_dtypes(include="number").columns
     df[numeric_columns] = df[numeric_columns].apply(pd.to_numeric, downcast='float')
-    # df.set_index("file", inplace=True)
-    # df = df.drop(columns=['file'])
-    # pd.set_option('display.float_format', '{:.2e}'.format) 
+
     if options.get("downsample"):
         df = df.sample(frac=options.get("downsample", 0.05))
         
@@ -1073,30 +1094,53 @@ def generate_report(data, **options):
         if col not in excluded_columns and not pd.api.types.is_numeric_dtype(df[col]):
             df[col] = df[col].astype('category')
 
+    match options.get("mode"):
+        case "face":
+            match options.get("engine"):
+                case "bqat":
+                    descriptions = {item.name: item.value for item in FaceSpecBQAT}
+                case "ofiq":
+                    descriptions = {item.name: item.value for item in FaceSpecOFIQ}
+                case "biqt":
+                    descriptions = {item.name: item.value for item in FaceSpecBIQT}
+                case _:
+                    descriptions = {}
+        case "fingerprint":
+            descriptions = {item.name: item.value for item in FingerprintSpecDefault}
+        case "iris":
+            descriptions = {item.name: item.value for item in IrisSpecDefault}
+        case "speech":
+            descriptions = {item.name: item.value for item in SpeechSpecDefault}
+        case _:
+            descriptions = {}
+
+    pd.set_option("display.float_format", "{:.4f}".format)
+
     ProfileReport(
         df,
         title=f"EDA Report (BQAT v{__version__})",
         explorative=True,
         minimal=options.get("minimal", False),
-        progress_bar=False,
-        # correlations={
-        #     "auto": {"calculate": False},
-        #     "pearson": {"calculate": False},
-        #     "spearman": {"calculate": True},
-        #     "kendall": {"calculate": False},
-        #     "phi_k": {"calculate": False},
-        #     "cramers": {"calculate": False},
-        # },
-        correlations=None,
+        # progress_bar=False,
+        correlations={
+            "auto": {"calculate": False},
+            "pearson": {"calculate": True},
+            "spearman": {"calculate": True},
+            "kendall": {"calculate": True},
+            "phi_k": {"calculate": False},
+            "cramers": {"calculate": False},
+        },
+        # correlations=None,
         vars={"num": {"low_categorical_threshold": 0}},
         html={
             "navbar_show": True,
+            # "full_width": True,
             "style": {
-                "full_width": True,
                 "theme": "simplex",
                 "logo": "https://www.biometix.com/wp-content/uploads/2020/10/logo.png",
             },
         },
+        variables={"descriptions": descriptions},
     ).to_file(temp)
 
     with open(temp, "r") as f: