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| import streamlit as st | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| import requests | |
| import io | |
| from transformers import pipeline | |
| from PIL import Image | |
| import requests | |
| st.title('Playing cards Image Analysis') | |
| ''' | |
| This next piece of code will hit GitHub for two csv files | |
| One is the original dataset, broken up into test, train, valid. | |
| The second csv is the test dataset, with the results after the models were run through the API | |
| ''' | |
| # Downloading the csv file from your GitHub account | |
| url = "https://huggingface.co/datasets/rwcuffney/autotrain-data-pick_a_card/raw/main/cards.csv" | |
| download = requests.get(url).content | |
| # Reading the downloaded content and turning it into a pandas data frame | |
| df = pd.read_csv(io.StringIO(download.decode('utf-8'))) | |
| #df = pd.read_csv('playing_cards/cards.csv').sort_values('class index') | |
| df_fulldataset=df | |
| # Downloading the csv file from your GitHub account | |
| url = "https://huggingface.co/datasets/rwcuffney/autotrain-data-pick_a_card/raw/main/ML_results.csv" | |
| download = requests.get(url).content | |
| # Reading the downloaded content and turning it into a pandas data frame | |
| df = pd.read_csv(io.StringIO(download.decode('utf-8'))) | |
| #df = pd.read_csv('playing_cards/cards.csv').sort_values('class index') | |
| df_test = df | |
| # Create the button | |
| if st.button('Click me to re-run code',key='RunCode_button'): | |
| # Call the function when the button is clicked | |
| st.experimental_rerun() | |
| st.header('Sample of the .csv data:') | |
| x = st.slider('Select a value',value=10,max_value=8000) | |
| st.table(df_fulldataset.sample(x)) | |
| ### HORIZONTAL BAR ### | |
| st.header('Distribution of the playing card images:') | |
| # Get the value counts of the 'labels' column | |
| value_counts = df_fulldataset.groupby('labels')['class index'].count().iloc[::-1] | |
| fig, ax = plt.subplots(figsize=(10,10)) | |
| # Create a bar chart of the value counts | |
| ax = value_counts.plot.barh() | |
| # Set the chart title and axis labels | |
| ax.set_title('Value Counts of Labels') | |
| ax.set_xlabel('Label') | |
| ax.set_ylabel('Count') | |
| # Show the chart | |
| st.pyplot(fig) | |
| ### PIE CHART ### | |
| st.header('Balance of Train,Valid,Test datasets:') | |
| # Get the value counts of the 'labels' column | |
| value_counts =df_fulldataset['data set'].value_counts() | |
| fig, ax = plt.subplots(figsize=(5,5) | |
| ) | |
| # Create a bar chart of the value counts | |
| ax = value_counts.plot.pie(autopct='%1.1f%%') | |
| # Set the chart title and axis labels | |
| # Show the chart | |
| st.pyplot(fig) | |
| models_run= ['SwinForImageClassification_24', | |
| 'ViTForImageClassification_22', | |
| 'SwinForImageClassification_21', | |
| 'ResNetForImageClassification_23', | |
| 'BeitForImageClassification_25'] | |
| pipeline_dict = dict( | |
| SwinForImageClassification_21="rwcuffney/autotrain-pick_a_card-3726099221", | |
| ViTForImageClassification_22="rwcuffney/autotrain-pick_a_card-3726099222", | |
| ResNetForImageClassification_23= "rwcuffney/autotrain-pick_a_card-3726099223", | |
| SwinForImageClassification_24 = "rwcuffney/autotrain-pick_a_card-3726099224", | |
| BeitForImageClassification_25="rwcuffney/autotrain-pick_a_card-3726099225") | |
| #### Try it out ### | |
| st.header("Try it out") | |
| ''' | |
| Warning: changing models takes a minute to download new model. | |
| You can use any image... try test/queen of hearts/4.jpg to see an example that | |
| Got different results with different models | |
| ''' | |
| ##### FORM ##### | |
| with st.form("api_form"): | |
| model = st.selectbox('Which model do you want to try?',models_run,key='select_box') | |
| uploaded_file = st.file_uploader("Choose a file") | |
| if uploaded_file is not None: | |
| # To read file as bytes: | |
| st.image(uploaded_file) | |
| image = Image.open(uploaded_file) | |
| submitted = st.form_submit_button("Submit") | |
| if submitted: | |
| pipeline = pipeline(task="image-classification", model=pipeline_dict[model]) | |
| def predict(image): | |
| predictions = pipeline(image) | |
| return {p["label"]: p["score"] for p in predictions} | |
| prediction = predict(image) | |
| st.write(prediction) | |
| #### FUNCTIONS #### | |
| import sklearn | |
| from sklearn import metrics | |
| import matplotlib.pyplot as plt | |
| index = ['accuracy_score','Weighted f1', 'Cohen Kappa','Matthews'] | |
| df_Metrics =pd.DataFrame(index=index) | |
| labels = df_test['labels'].unique() | |
| ### FUNCTION TO SHOW THE METRICS | |
| def show_metrics(test,pred,name): | |
| from sklearn import metrics | |
| my_Name = name | |
| my_Accuracy_score=metrics.accuracy_score(test, pred) | |
| #my_ROC_AUC_score= roc_auc_score(y, model.predict_proba(X), multi_class='ovr') | |
| my_Weighted_f1= metrics.f1_score(test, pred,average='weighted') | |
| my_Cohen_Kappa = metrics.cohen_kappa_score(test, pred) | |
| my_Matthews_coefficient=metrics.matthews_corrcoef(test, pred) | |
| st.header(f'Metrics for {my_Name}:') | |
| report =metrics.classification_report(test, pred, output_dict=True) | |
| df_report = pd.DataFrame(report).transpose() | |
| st.dataframe(df_report ) | |
| st.write(f'Accuracy Score........{metrics.accuracy_score(test, pred):.4f}\n\n' \ | |
| #f'ROC AUC Score.........{my_ROC_AUC_score:.4f}\n\n' \ | |
| f'Weighted f1 score.....{my_Weighted_f1:.4f}\n\n' \ | |
| f'Cohen Kappa...........{my_Cohen_Kappa:.4f}\n\n' \ | |
| f'Matthews Coefficient..{my_Matthews_coefficient:.4f}\n\n') | |
| my_List = [my_Accuracy_score, my_Weighted_f1, my_Cohen_Kappa, my_Matthews_coefficient] | |
| df_Metrics[my_Name] = my_List | |
| cfm= metrics.confusion_matrix(test, pred) | |
| st.caption(f'Confusion Matrix: {my_Name}') | |
| cmd = metrics.ConfusionMatrixDisplay(cfm,display_labels=labels) | |
| fig, ax = plt.subplots(figsize=(15,15)) | |
| ax = cmd.plot(ax=ax, | |
| colorbar=False, | |
| values_format = '.0f', | |
| cmap='Reds')#='tab20')# see color options here https://matplotlib.org/stable/tutorials/colors/colormaps.html | |
| plt.xticks(rotation=90) | |
| st.pyplot(fig) | |
| st.header('Let\'s see how the models performed') | |
| ''' | |
| The next part of the code will analyze the full dataset. | |
| Choose all five models to compare them all | |
| ''' | |
| ##### FORM ##### | |
| with st.form("my_form"): | |
| st.write("You can choose from 1 to 5 models") | |
| selected_options = st.multiselect( | |
| 'Which models would you like to analyze?', models_run) | |
| submitted = st.form_submit_button("Submit") | |
| if submitted: | |
| st.write('you selected',selected_options) | |
| ###Show the metrics for each dataset: | |
| test = df_test['labels'] | |
| #for m in models_run: | |
| for m in selected_options: | |
| pred = df_test[m] | |
| show_metrics(test,pred,m) | |
| st.header('Metrics for all models:') | |
| st.table(df_Metrics) | |
| #### GRAPH THE RESULTS ### | |
| import seaborn as sns | |
| # Reshape the dataframe into long format using pd.melt() | |
| #subset_df = pd.melt(df_Metrics[['SwinForImageClassification_24', | |
| #'ViTForImageClassification_22', 'SwinForImageClassification_21', 'ResNetForImageClassification_23', 'BeitForImageClassification_25']].reset_index(), id_vars='index', var_name='Model', value_name='Score') | |
| subset_df = pd.melt(df_Metrics[selected_options].reset_index(), id_vars='index', var_name='Model', value_name='Score') | |
| sns.set_style('whitegrid') | |
| ax=sns.catplot(data=subset_df, | |
| x='index', | |
| y='Score', | |
| hue='Model', | |
| kind='bar', | |
| palette='Blues', | |
| aspect=2) | |
| plt.xlabel('Clusters') | |
| plt.ylabel('Scores') | |
| fig = ax.figure | |
| st.pyplot(fig) |