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.gitignore

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+flagged/
+*.pt
+gradio_cached_examples/

README.md

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----
-title: Lepdect
-emoji: 📊
-colorFrom: pink
-colorTo: indigo
-sdk: gradio
-sdk_version: 5.23.0
-app_file: app.py
-pinned: false
-short_description: leprosy detection
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Leprosy Detection
+emoji: ⚡
+colorFrom: red
+colorTo: gray
+sdk: gradio
+sdk_version: 3.44.4
+app_file: app.py
+pinned: false
+license: cc-by-4.0
+---
+
+# Leprosy Detection App
+
+Leprosy Gradio Detection web application. In this application, I have used a fine tuned YOLOv5s model to detect Leprosy samples based on images. The application uses gradio as the platform and can also be used in the [Huggingface online hosting application](https://huggingface.co/spaces/Arekku21/Leprosy-Detection). 
+
+## Overview
+[Leprosy](https://en.wikipedia.org/wiki/Leprosy), also known as Hansen's disease, is a chronic infectious disease that primarily affects the skin and peripheral nerves. The model used is a YOLO model from [Ultralytics](https://github.com/ultralytics/yolov5) with their version YOLOv5. 
+
+### Features
+
+- Upload an image to the app.
+- Utilizes a fine-tuned YOLOv5s model for leprosy detection.
+- Detect and label leprosy regions in the uploaded image.
+
+## Prerequisites
+
+Before running the application, make sure you have the following prerequisites installed on your system:
+
+- Python 3.x 
+- Git
+- Gradio
+- Pip package manager
+- Conda Virtual environment (optional but recommended)
+
+### Installation Steps and Running the app
+
+To install the required Python libraries, navigate to the project directory and run the following command:
+
+#### Step 1 Clone the repository
+```
+# Make sure you have git-lfs installed (https://git-lfs.com)
+git lfs install
+git clone https://huggingface.co/spaces/Arekku21/Leprosy-Detection
+
+# if you want to clone without large files – just their pointers
+# prepend your git clone with the following env var:
+GIT_LFS_SKIP_SMUDGE=1
+```
+
+#### Step 2 Install requirements
+```
+#navigate to your cloned repository and location of requirmenents.txt
+pip install -r requirements.txt
+```
+
+#### Step 3 Run the app
+```
+#ensure that you are using the right environment or have all the requirements installed
+#ensure that you are navigated to the cloned repository
+python app.py
+```
+
+#### Step 4 Using the app
+Your terminal should look like this and follow the local host URL link to use the application. 
+```
+Downloading: "https://github.com/ultralytics/yolov5/zipball/master" to C:\Users\master.zip
+
+YOLOv5  2023-9-28 Python-3.9.18 torch-2.0.1+cpu CPU
+
+Fusing layers... 
+Model summary: 157 layers, 7015519 parameters, 0 gradients, 15.8 GFLOPs
+Adding AutoShape...
+Running on local URL:  http://127.0.0.1:7860
+```

app.py

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+import gradio as gr
+import cv2
+import requests
+import os
+
+import torch
+from PIL import Image
+import tempfile
+
+import numpy as np
+
+import math
+
+
+file_urls = [
+    'https://www.dropbox.com/scl/fi/onrg1u9tqegh64nsfmxgr/lp2.jpg?rlkey=2vgw5n6abqmyismg16mdd1v3n&dl=1',
+    'https://www.dropbox.com/scl/fi/xq103ic7ovuuei3l9e8jf/lp1.jpg?rlkey=g7d9khyyc6wplv0ljd4mcha60&dl=1',
+    'https://www.dropbox.com/scl/fi/fagkh3gnio2pefdje7fb9/Non_Leprosy_210823_86_jpg.rf.5bb80a7704ecc6c8615574cad5d074c5.jpg?rlkey=ks8afue5gsx5jqvxj3u9mbjmg&dl=1',
+    'https://www.dropbox.com/scl/fi/gh4zotrzic5y00ok3crje/Non_Leprosy_210823_46_jpg.rf.76c20cb340114a98618ade07c3e6b413.jpg?rlkey=pxdjlhxipmsd12gr4veyg691v&dl=1',
+    'https://www.dropbox.com/scl/fi/r8vgo1xrledlsw7rxq4ar/Tropmed-91-216-g001.jpg?rlkey=6iajn3xoa6zsxtxh4exq4z3p5&dl=1',
+    'https://www.dropbox.com/scl/fi/kxv0q49e92h3fr7ihvqbu/Non_Leprosy_210823_8_jpg.rf.e2d44b96e1bb9b5111b780adec5ba94a.jpg?rlkey=g25iq6vbwqs1glusyv1lgv5a2&dl=1'
+]
+
+def download_file(url, save_name):
+    url = url
+    if not os.path.exists(save_name):
+        file = requests.get(url)
+        open(save_name, 'wb').write(file.content)
+
+for i, url in enumerate(file_urls):
+    if 'mp4' in file_urls[i]:
+        download_file(
+            file_urls[i],
+            f"video.mp4"
+        )
+    else:
+        download_file(
+            file_urls[i],
+            f"image_{i}.jpg"
+        )
+
+path = "obj.pt"
+model = torch.hub.load('ultralytics/yolov5', 'custom', path=path, force_reload=True)
+img_path  = [['image_0.jpg'], ['image_1.jpg'],['image_2.jpg'],['image_3.jpg'],['image_4.jpg'],['image_5.jpg'],]
+video_path = [['video.mp4']]
+
+def show_preds_image_and_labels(image_path):
+    image = cv2.imread(image_path)
+
+    results = model(image_path)
+
+    pandas_result = results.pandas().xyxy[0]
+
+    array_results = pandas_result.to_numpy()
+
+    array_results = array_results.tolist()
+
+    #print raw results
+    print("\nRaw results: ", array_results)
+
+    array_bounding_box= []
+
+    array_model_result = []
+
+    array_model_confidence = []
+
+    #for labelling bounding box
+    for item in array_results:
+        array_bounding_box.append([item[0],item[1],item[2],item[3]])
+        array_model_result.append(item[6])
+        array_model_confidence.append(str(round(item[4],1)*100))
+
+    for numbers in range(len(array_model_result)):
+      x1, y1 = int(math.floor(array_bounding_box[numbers][0])), int(math.floor(array_bounding_box[numbers][1]))  # top-left corner
+      x2, y2 = int(math.floor(array_bounding_box[numbers][2])), int(math.floor(array_bounding_box[numbers][3]))  # bottom-right corner
+
+      if array_model_result[numbers] == "Lep":
+        # draw a rectangle over the image using the bounding box coordinates
+
+        #if the value of leprosy conf is < 0.45 then label it as NLp to show the max voting value
+        if  float(array_model_confidence[numbers]) > 45.0:
+
+          cv2.rectangle(image, (x1, y1), (x2, y2), (0, 0,255), 1)
+          cv2.putText(image, array_model_result[numbers] + " " + array_model_confidence[numbers] + "%", (x1, y1-5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,255), 1)
+
+        elif float(array_model_confidence[numbers]) < 45.0:
+
+          cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255,0), 1)
+          cv2.putText(image, "Non Lep" + " " + array_model_confidence[numbers] + "%", (x1, y1-5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,255,0), 1)
+
+      elif array_model_result[numbers] == "Non Lep":
+        
+        # draw a rectangle over the image using the bounding box coordinates
+        cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255,0), 1)
+        cv2.putText(image, array_model_result[numbers] + " " + array_model_confidence[numbers] + "%", (x1, y1-5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,255,0), 1)
+
+    #labelling dictionary
+
+    array_results_conf_large = []
+
+    for yolo_results in array_results:
+      if yolo_results[4] > 0.45:
+        array_results_conf_large.append(yolo_results)
+
+    print("Large results only: ",array_results_conf_large)
+
+    num_lep = 0
+    confidence_lep = 0
+
+    num_non_lep = 0
+    confidence_non_lep = 0
+
+    for item in array_results_conf_large:
+        if item[6] == "Lep":
+          num_lep+=1
+          confidence_lep += item[4]
+        elif item[6] == "Non Lep":
+          num_non_lep+=1
+          confidence_non_lep += item[4]
+
+    labels = {}
+
+    #if num_lep is more than non lep
+    if num_lep > num_non_lep:
+        labels["Leprosy"] = round(confidence_lep/num_lep,2)
+
+        if num_non_lep != 0:
+            labels["Non Leprosy"] = round(confidence_non_lep/num_non_lep,2) 
+        else:
+            lower_score_lep_num = 0
+            lower_score_lep_conf = 0.0
+            
+            for results_index in range(len(array_model_confidence)):
+                if array_model_result[results_index] == "Lep":
+                  if float(array_model_confidence[results_index]) < 45.0:
+                      lower_score_lep_num+=1
+                      lower_score_lep_conf+=float(array_model_confidence[results_index])
+
+            if lower_score_lep_num != 0:
+                labels["Non Leprosy"] = round((lower_score_lep_conf/lower_score_lep_num)/100,2) 
+            else:
+                labels["Non Leprosy"] = 0.0 
+                        
+    #if num_non_lep is more than lep
+    elif num_lep < num_non_lep:
+        labels["Non Leprosy"] = round(confidence_non_lep/num_non_lep,2)
+
+        if num_lep != 0:
+            labels["Leprosy"] = round(confidence_lep/num_lep,2)
+        else:
+            labels["Leprosy"] = 0.0 
+        
+    #if num_non_lep and num_lep is equal but they are equal coz they are both 0
+    elif num_lep == num_non_lep and num_lep == 0:
+        labels["Others"] = 0.9
+
+    #if num_non_lep and num_lep is equal but they are equal coz they are both 0
+    elif num_lep == num_non_lep:
+
+        #incase of a tie in quantity we compare the mean probability of each
+        confidence_lep = round(confidence_lep/num_lep,2)
+        confidence_non_lep = round(confidence_non_lep/num_non_lep,2)
+        
+        if confidence_lep > confidence_non_lep:
+            labels["Leprosy"] = confidence_lep
+            labels["Non Leprosy"] = confidence_non_lep
+        
+        elif confidence_lep < confidence_non_lep:
+            labels["Leprosy"] = confidence_lep
+            labels["Non Leprosy"] = confidence_non_lep
+        
+        elif confidence_lep == confidence_non_lep:
+            labels["Leprosy"] = confidence_lep
+            labels["Non Leprosy"] = confidence_non_lep
+
+    return cv2.cvtColor(image, cv2.COLOR_BGR2RGB),labels
+
+inputs_image = [
+    gr.components.Image(type="filepath", label="Input Image"),
+]
+outputs_image = [
+    gr.components.Image(type="numpy", label="Output Image"),
+    gr.Label(label="Labels with Confidence"),
+
+]
+interface_image = gr.Interface(
+    fn=show_preds_image_and_labels,
+    inputs=inputs_image,
+    outputs=outputs_image,
+    title="Leprosy Detection",
+    examples=img_path,
+    cache_examples=False,
+)
+    
+gr.TabbedInterface(
+    [interface_image],
+    tab_names=['Image inference']
+).queue().launch()

obj.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3be9d4f559d07d3bdb661ff619919afcfa2cfbb0a46b92ebf3aaca835b7383f2
+size 14446333

requirements.txt

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+# YOLOv5 requirements
+# Usage: pip install -r requirements.txt
+
+# Base ------------------------------------------------------------------------
+gitpython>=3.1.30
+matplotlib>=3.3
+numpy>=1.22.2
+opencv-python>=4.1.1
+Pillow>=7.1.2
+psutil  # system resources
+PyYAML>=5.3.1
+requests>=2.23.0
+scipy>=1.4.1
+thop>=0.1.1  # FLOPs computation
+torch>=1.8.0  # see https://pytorch.org/get-started/locally (recommended)
+torchvision>=0.9.0
+tqdm>=4.64.0
+ultralytics>=8.0.147
+# protobuf<=3.20.1  # https://github.com/ultralytics/yolov5/issues/8012
+
+# Logging ---------------------------------------------------------------------
+# tensorboard>=2.4.1
+# clearml>=1.2.0
+# comet
+
+# Plotting --------------------------------------------------------------------
+pandas>=1.1.4
+seaborn>=0.11.0
+
+# Export ----------------------------------------------------------------------
+# coremltools>=6.0  # CoreML export
+# onnx>=1.10.0  # ONNX export
+# onnx-simplifier>=0.4.1  # ONNX simplifier
+# nvidia-pyindex  # TensorRT export
+# nvidia-tensorrt  # TensorRT export
+# scikit-learn<=1.1.2  # CoreML quantization
+# tensorflow>=2.4.0  # TF exports (-cpu, -aarch64, -macos)
+# tensorflowjs>=3.9.0  # TF.js export
+# openvino-dev>=2023.0  # OpenVINO export
+
+# Deploy ----------------------------------------------------------------------
+setuptools>=65.5.1 # Snyk vulnerability fix
+# tritonclient[all]~=2.24.0
+
+# Extras ----------------------------------------------------------------------
+# ipython  # interactive notebook
+# mss  # screenshots
+# albumentations>=1.0.3
+# pycocotools>=2.0.6  # COCO mAP
+
+gradio