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app.py

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+import gradio as gr
+import pandas as pd
+import pickle
+import shap
+import xgboost
+import matplotlib
+import matplotlib.pyplot as plt
+import io
+matplotlib.use('Agg')
+pc = ["Enterprise System", "Management Information System", "Safety Critical System", "Transaction Processing System" ]
+rc = ['Constraints','Functional','Interfaces','Performance','Reliability&Availability','Safety','Security','Standards','Supportability','Usability']
+rtc = ['Budget','Business','Cost','Design','Functional Validity','Organizational Environment','Overdrawn Budget','People','Performance','Personal','Planning&Control','Process','Project complexity','Quality','Requirement','Resource availability','Schedule','Software','Team','TimeDimension','Unrealistic Requirements','User']
+mor = ['Extreme','High','Low','Medium','Negligible','Very High','Very Low']
+imp = ["Low", "Catastrophic", "High", "Insignificant", "High", "Moderate"]
+dor = ['Cost','Estimations','Organizational Environment','Organizational Requirements','Planning and Control','Project Complexity','Project complexity','Requirements','Schedule','Software Requirement','Team','User','Planning and Control']
+def ml_function(probability, affecting_no_of_modules,
+                fixing_duration, project_category, requirement_category, risk_target_category,
+                magnitude_of_risk, impact, dimension_of_risk, fix_cost_percent_of_project):
+    data = list()
+    data.append(probability)
+    data.append(affecting_no_of_modules)
+    data.append(fixing_duration)
+    data.extend([bool(c in project_category) for c in pc])
+    data.extend([bool(c in requirement_category) for c in rc])
+    data.extend([bool(c in risk_target_category) for c in rtc])
+    data.extend([bool(c in magnitude_of_risk) for c in mor])
+    data.extend([bool(c in impact) for c in imp])
+    data.extend([bool(c in dimension_of_risk) for c in dor])
+    fix_cost_percent_of_project_one_hot = [False] * 10
+    fix_cost_percent_of_project_one_hot[fix_cost_percent_of_project] = True
+    data.extend(fix_cost_percent_of_project_one_hot)
+
+    df = pd.DataFrame([data])
+    df.columns = ['Probability', 'Afftecting No of Modules', 'Fixing Duration (Days)',
+       'project Category_Enterprise System',
+       'project Category_Management Information System',
+       'project Category_Safety Critical System',
+       'project Category_Transaction Processing System',
+       'Requirement Category_Constraints', 'Requirement Category_Functional',
+       'Requirement Category_Interfaces', 'Requirement Category_Performance',
+       'Requirement Category_Reliability & Availability',
+       'Requirement Category_Safety', 'Requirement Category_Security',
+       'Requirement Category_Standards', 'Requirement Category_Supportability',
+       'Requirement Category_Usability', 'Risk Target Category_Budget',
+       'Risk Target Category_Business', 'Risk Target Category_Cost',
+       'Risk Target Category_Design',
+       'Risk Target Category_FunctionalValidity',
+       'Risk Target Category_Organizational Environment',
+       'Risk Target Category_Overdrawn Budget', 'Risk Target Category_People',
+       'Risk Target Category_Performance', 'Risk Target Category_Personal',
+       'Risk Target Category_Planning & Control',
+       'Risk Target Category_Process',
+       'Risk Target Category_Project complexity',
+       'Risk Target Category_Quality', 'Risk Target Category_Requirement',
+       'Risk Target Category_Resource availability',
+       'Risk Target Category_Schedule', 'Risk Target Category_Software',
+       'Risk Target Category_Team', 'Risk Target Category_Time Dimension',
+       'Risk Target Category_Unrealistic Requirements',
+       'Risk Target Category_User', 'Magnitude of Risk_Extreme',
+       'Magnitude of Risk_High', 'Magnitude of Risk_Low',
+       'Magnitude of Risk_Medium', 'Magnitude of Risk_Negligible',
+       'Magnitude of Risk_Very High', 'Magnitude of Risk_Very Low',
+       'Impact_Low', 'Impact_catastrophic', 'Impact_high',
+       'Impact_insignificant', 'Impact_moderate', 'Dimension of Risk_Cost',
+       'Dimension of Risk_Estimations',
+       'Dimension of Risk_Organizational Environment',
+       'Dimension of Risk_Organizational Requirements',
+       'Dimension of Risk_Planning and Control',
+       'Dimension of Risk_Project Complexity',
+       'Dimension of Risk_Project complexity',
+       'Dimension of Risk_Requirements', 'Dimension of Risk_Schedule',
+       'Dimension of Risk_Software Requirement', 'Dimension of Risk_Team',
+       'Dimension of Risk_User', 'Dimension of Risk_planning and control',
+       'Fix Cost (\% of Project)_0', 'Fix Cost (\% of Project)_1',
+       'Fix Cost (\% of Project)_10', 'Fix Cost (\% of Project)_11',
+       'Fix Cost (\% of Project)_2', 'Fix Cost (\% of Project)_21',
+       'Fix Cost (\% of Project)_3', 'Fix Cost (\% of Project)_4',
+       'Fix Cost (\% of Project)_5', 'Fix Cost (\% of Project)_6',
+       'Fix Cost (\% of Project)_?']
+    df = df.reset_index(drop=True)
+    for col in df.columns:
+        if df[col].dtype == 'object':
+            df[col] = pd.to_numeric(df[col])
+    xgb_model_loaded = pickle.load(open('xgboost_project_risk_prediction.pickle', "rb"))
+    pred = xgb_model_loaded.predict(df[:1])
+    return str(pred[0])
+
+# Define the Gradio interface with appropriate input components
+iface = gr.Interface(
+    fn=ml_function,
+    inputs=[
+        gr.inputs.Number(label="Probability"),
+        gr.inputs.Number(label="Affecting No of Modules"),
+        gr.inputs.Number(label="Fixing Duration (Days)"),
+        gr.inputs.Radio(choices=pc, label="Project Category"),
+        gr.inputs.Radio(choices=rc, label="Requirement Category"),
+        gr.inputs.Radio(choices=rtc, label="Risk Target Category"),
+        gr.inputs.Radio(choices=mor, label="Magnitude of Risk"),
+        gr.inputs.Radio(choices=imp, label="Impact"),
+        gr.inputs.Radio(choices=dor, label="Dimension of Risk"),
+        gr.inputs.Slider(minimum=0, maximum=10,step=1, label="Fix Cost (% of Project)")
+    ],
+    outputs=[
+        gr.outputs.Label(label="Risk Level")
+    ]
+)
+
+# Run the Gradio app
+iface.launch()

requirements.txt

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+gradio
+numpy
+scikit-learn
+shap
+xgboost
+matplotlib

xgboost_project_risk_prediction.pickle

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e97474ad21ba60cb48b016a30737a3ca065d8f367abd27c02e9220b5be8d9cc5
+size 213223