Upload folder using huggingface_hub

.ipynb_checkpoints/Untitled-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

.ipynb_checkpoints/gradio_hearttack_app-checkpoint.py

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+import gradio as gr
+import xgboost as xgb
+import joblib
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+import pandas as pd
+
+# Load the model and the scaler
+model = joblib.load('best_XGB.pkl')
+scaler = joblib.load('scaler.pkl')  # Load the scaler if you saved it during training
+cutoff = 0.42  # Custom cutoff probability
+
+# Define the prediction function with preprocessing and scaling
+def predict_heart_attack(Gender, age, cigsPerDay, BPMeds, prevalentHyp, diabetes, totChol, sysBP, diaBP, BMI, heartRate, glucose):
+    # Define feature names in the same order as the training data
+    feature_names = ['Gender', 'age', 'cigsPerDay', 'BPMeds', 'prevalentHyp', 'diabetes', 'totChol', 'sysBP', 'diaBP', 'BMI', 'heartRate', 'glucose']
+    # Create a DataFrame with the correct feature names for prediction
+    features = pd.DataFrame([[Gender, age, cigsPerDay, BPMeds, prevalentHyp, diabetes, totChol, sysBP, diaBP, BMI, heartRate, glucose]],      columns=feature_names)
+    
+    # Standardize the features (scaling)
+    scaled_features = scaler.transform(features)
+    
+    # Predict probabilities
+    proba = model.predict_proba(scaled_features)[:, 1]  # Probability of class 1 (heart attack)
+    
+    # Apply custom cutoff
+    if proba[0] >= cutoff:
+        prediction_class = 1
+    else:
+        prediction_class = 0
+    
+    result = f"Predicted Probability: {proba[0]*100:.2f}%. Predicted Class with cutoff {cutoff}: {prediction_class}"
+    
+    return result
+
+# Create the Gradio interface with preprocessing and prediction logic
+with gr.Blocks() as app:
+    with gr.Row():
+        with gr.Column():
+            Gender = gr.Radio([0, 1], label="Gender (0=Female, 1=Male)")
+            cigsPerDay = gr.Slider(0, 40, step=1, label="Cigarettes per Day")
+            prevalentHyp = gr.Radio([0, 1], label="Prevalent Hypertension (0=No, 1=Yes)")
+            totChol = gr.Slider(100, 400, step=1, label="Total Cholesterol in mg/dl")
+            diaBP = gr.Slider(60, 120, step=1, label="Diastolic/Higher BP")
+            heartRate = gr.Slider(50, 120, step=1, label="Heart Rate")
+        
+        with gr.Column():
+            age = gr.Slider(20, 80, step=1, label="Age (years)")
+            BPMeds = gr.Radio([0, 1], label="On BP Medications (0=No, 1=Yes)")
+            diabetes = gr.Radio([0, 1], label="Diabetes (0=No, 1=Yes)")
+            sysBP = gr.Slider(90, 200, step=1, label="Systolic BP/Lower BP")
+            BMI = gr.Slider(15, 40, step=0.1, label="Body Mass Index (BMI) in kg/m2")
+            glucose = gr.Slider(50, 250, step=1, label="Fasting Glucose Level")
+    
+    # Center-aligned prediction output
+    with gr.Row():
+        gr.HTML("<div style='text-align: center; width: 100%'>Heart Attack Prediction</div>")
+    
+    with gr.Row():
+        prediction_output = gr.Textbox(label="", interactive=False, elem_id="prediction_output")
+    
+    # Link inputs and prediction output
+    submit_btn = gr.Button("Submit")
+    submit_btn.click(fn=predict_heart_attack, inputs=[Gender, age, cigsPerDay, BPMeds, prevalentHyp, diabetes, totChol, sysBP, diaBP, BMI, heartRate, glucose], outputs=prediction_output)
+
+app.launch(share = True)

.ipynb_checkpoints/requirements-checkpoint.txt

@@ -0,0 +1,247 @@
+absl-py==2.1.0
+aiofiles==23.2.1
+alembic==1.13.3
+altair==5.3.0
+aniso8601==9.0.1
+annotated-types==0.7.0
+anyio==4.6.2.post1
+asn1crypto==1.5.1
+asttokens==2.4.1
+astunparse==1.6.3
+attrs==23.2.0
+Automat==22.10.0
+bayesian-optimization==1.4.3
+beautifulsoup4==4.12.3
+blinker==1.8.2
+cachetools==5.3.3
+certifi==2024.2.2
+cffi==1.16.0
+charset-normalizer==3.3.2
+chromedriver-autoinstaller==0.6.4
+click==8.1.7
+cloudpickle==3.1.0
+colorama==0.4.6
+comm==0.2.2
+constantly==23.10.4
+contourpy==1.2.1
+convertdate==2.4.0
+cryptography==43.0.0
+cssselect==1.2.0
+cycler==0.12.1
+Cython==3.0.10
+dash==2.17.0
+dash-core-components==2.0.0
+dash-html-components==2.0.0
+dash-table==5.0.0
+databricks-sdk==0.34.0
+dearpygui==1.11.1
+debugpy==1.8.1
+decorator==5.1.1
+defusedxml==0.7.1
+Deprecated==1.2.14
+dnspython==2.6.1
+docker==7.1.0
+docutils==0.21.2
+et-xmlfile==1.1.0
+executing==2.0.1
+fakeredis==2.23.2
+fastapi==0.115.2
+fastjsonschema==2.19.1
+ffmpy==0.4.0
+filelock==3.15.4
+Flask==3.0.3
+flatbuffers==24.3.25
+fonttools==4.51.0
+frozendict==2.4.4
+fsspec==2024.9.0
+gast==0.5.4
+gitdb==4.0.11
+GitPython==3.1.43
+google-auth==2.35.0
+google-pasta==0.2.0
+gradio==5.1.0
+gradio_client==1.4.0
+graphene==3.3
+graphql-core==3.2.5
+graphql-relay==3.2.0
+graphviz==0.20.3
+greenlet==3.0.3
+grpcio==1.64.1
+gunicorn==23.0.0
+h11==0.14.0
+h5py==3.11.0
+holidays==0.53
+html5lib==1.1
+httpcore==1.0.6
+httpx==0.27.2
+huggingface-hub==0.25.2
+hyperlink==21.0.0
+idna==3.7
+imbalanced-learn==0.12.4
+imblearn==0.0
+importlib_metadata==7.1.0
+incremental==24.7.0
+install==1.3.5
+ipykernel==6.29.4
+ipython==8.24.0
+itemadapter==0.9.0
+itemloaders==1.3.1
+itsdangerous==2.2.0
+jedi==0.19.1
+Jinja2==3.1.4
+jmespath==1.0.1
+joblib==1.4.2
+jsonschema==4.22.0
+jsonschema-specifications==2023.12.1
+jupyter_client==8.6.1
+jupyter_core==5.7.2
+keras==3.3.3
+Kivy==2.3.0
+Kivy-Garden==0.1.5
+kiwisolver==1.4.5
+libclang==18.1.1
+lxml==5.2.2
+Mako==1.3.5
+Markdown==3.6
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+matplotlib==3.8.4
+matplotlib-inline==0.1.7
+mdurl==0.1.2
+ml-dtypes==0.3.2
+mlflow==2.17.0
+mlflow-skinny==2.17.0
+mlxtend==0.23.1
+multitasking==0.0.11
+namex==0.0.8
+nbformat==5.10.4
+nest-asyncio==1.6.0
+networkx==3.3
+numpy==1.26.4
+nvidia-cublas-cu12==12.3.4.1
+nvidia-cuda-cupti-cu12==12.3.101
+nvidia-cuda-nvcc-cu12==12.3.107
+nvidia-cuda-nvrtc-cu12==12.3.107
+nvidia-cuda-runtime-cu12==12.3.101
+nvidia-cudnn-cu12==8.9.7.29
+nvidia-cufft-cu12==11.0.12.1
+nvidia-curand-cu12==10.3.4.107
+nvidia-cusolver-cu12==11.5.4.101
+nvidia-cusparse-cu12==12.2.0.103
+nvidia-nccl-cu12==2.19.3
+nvidia-nvjitlink-cu12==12.3.101
+openpyxl==3.1.2
+opentelemetry-api==1.27.0
+opentelemetry-sdk==1.27.0
+opentelemetry-semantic-conventions==0.48b0
+opt-einsum==3.3.0
+optree==0.11.0
+orjson==3.10.7
+outcome==1.3.0.post0
+packaging==24.0
+pandas==2.2.2
+parsel==1.9.1
+parso==0.8.4
+patsy==0.5.6
+peewee==3.17.5
+pexpect==4.9.0
+pg8000==1.31.2
+pillow==10.3.0
+platformdirs==4.2.1
+plotly==5.22.0
+pmdarima==2.0.4
+prompt-toolkit==3.0.43
+Protego==0.3.1
+protobuf==4.25.3
+psutil==5.9.8
+ptyprocess==0.7.0
+pure-eval==0.2.2
+pyarrow==16.1.0
+pyasn1==0.6.0
+pyasn1_modules==0.4.0
+pycparser==2.22
+pydantic==2.9.2
+pydantic_core==2.23.4
+pydeck==0.9.1
+PyDispatcher==2.0.7
+pydot==2.0.0
+pydub==0.25.1
+Pygments==2.18.0
+PyMeeus==0.5.12
+pymongo==4.7.3
+pyOpenSSL==24.2.1
+pyparsing==3.1.2
+PySocks==1.7.1
+pystan==2.19.1.1
+python-dateutil==2.9.0.post0
+python-dotenv==1.0.1
+python-multipart==0.0.12
+pytz==2024.1
+PyYAML==6.0.2
+pyzmq==26.0.3
+queuelib==1.7.0
+redis==5.0.6
+referencing==0.35.1
+requests==2.31.0
+requests-file==2.1.0
+retrying==1.3.4
+rich==13.7.1
+rpds-py==0.18.1
+rsa==4.9
+ruff==0.7.0
+scikit-learn==1.4.2
+scipy==1.13.0
+scramp==1.4.5
+Scrapy==2.11.2
+seaborn==0.13.2
+selenium==4.23.1
+semantic-version==2.10.0
+service-identity==24.1.0
+shellingham==1.5.4
+six==1.16.0
+smmap==5.0.1
+sniffio==1.3.1
+sortedcontainers==2.4.0
+soupsieve==2.5
+SQLAlchemy==2.0.31
+sqlparse==0.5.1
+stack-data==0.6.3
+starlette==0.40.0
+statsmodels==0.14.2
+streamlit==1.36.0
+tenacity==8.3.0
+tensorboard==2.17.1
+tensorboard-data-server==0.7.2
+tensorflow==2.17.0
+tensorflow-io-gcs-filesystem==0.37.0
+termcolor==2.4.0
+threadpoolctl==3.5.0
+tldextract==5.1.2
+toml==0.10.2
+tomlkit==0.12.0
+toolz==0.12.1
+tornado==6.4
+tqdm==4.66.5
+traitlets==5.14.3
+trio==0.26.0
+trio-websocket==0.11.1
+Twisted==24.3.0
+typer==0.12.5
+typing_extensions==4.11.0
+tzdata==2024.1
+urllib3==2.2.1
+uvicorn==0.32.0
+w3lib==2.2.1
+watchdog==4.0.1
+wcwidth==0.2.13
+webdriver-manager==4.0.2
+webencodings==0.5.1
+websocket-client==1.8.0
+websockets==12.0
+Werkzeug==3.0.3
+wrapt==1.16.0
+wsproto==1.2.0
+xgboost==2.0.3
+yfinance==0.2.40
+zipp==3.19.0
+zope.interface==6.4.post2

README.md

@@ -1,12 +1,6 @@
 ---
-title: Gradio App
-emoji: 👀
-colorFrom: indigo
-colorTo: green
+title: gradio_app
+app_file: gradio_hearttack_app.py
 sdk: gradio
 sdk_version: 5.1.0
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

Untitled.ipynb

@@ -0,0 +1,67 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1110063-e160-456d-ae0b-80d9cae3b8a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ecube/basicds_py311/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "import gradio as gr\n",
+    "import xgboost as xgb\n",
+    "import joblib\n",
+    "import numpy as np\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5e2655ee-1663-44f1-b05c-708b32c23e6a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip freeze >> requirements.txt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c71ec40a-634c-4735-bba9-31da888e3a5b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "basicds_py311",
+   "language": "python",
+   "name": "basicds_py311"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

best_XGB.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3d5422417966b1cc081d0b6e9772e6cee262ee72f203853be4a28d53859fbbcf
+size 1347623

gradio_hearttack_app.py

@@ -0,0 +1,66 @@
+import gradio as gr
+import xgboost as xgb
+import joblib
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+import pandas as pd
+
+# Load the model and the scaler
+model = joblib.load('best_XGB.pkl')
+scaler = joblib.load('scaler.pkl')  # Load the scaler if you saved it during training
+cutoff = 0.42  # Custom cutoff probability
+
+# Define the prediction function with preprocessing and scaling
+def predict_heart_attack(Gender, age, cigsPerDay, BPMeds, prevalentHyp, diabetes, totChol, sysBP, diaBP, BMI, heartRate, glucose):
+    # Define feature names in the same order as the training data
+    feature_names = ['Gender', 'age', 'cigsPerDay', 'BPMeds', 'prevalentHyp', 'diabetes', 'totChol', 'sysBP', 'diaBP', 'BMI', 'heartRate', 'glucose']
+    # Create a DataFrame with the correct feature names for prediction
+    features = pd.DataFrame([[Gender, age, cigsPerDay, BPMeds, prevalentHyp, diabetes, totChol, sysBP, diaBP, BMI, heartRate, glucose]],      columns=feature_names)
+    
+    # Standardize the features (scaling)
+    scaled_features = scaler.transform(features)
+    
+    # Predict probabilities
+    proba = model.predict_proba(scaled_features)[:, 1]  # Probability of class 1 (heart attack)
+    
+    # Apply custom cutoff
+    if proba[0] >= cutoff:
+        prediction_class = 1
+    else:
+        prediction_class = 0
+    
+    result = f"Predicted Probability: {proba[0]*100:.2f}%. Predicted Class with cutoff {cutoff}: {prediction_class}"
+    
+    return result
+
+# Create the Gradio interface with preprocessing and prediction logic
+with gr.Blocks() as app:
+    with gr.Row():
+        with gr.Column():
+            Gender = gr.Radio([0, 1], label="Gender (0=Female, 1=Male)")
+            cigsPerDay = gr.Slider(0, 40, step=1, label="Cigarettes per Day")
+            prevalentHyp = gr.Radio([0, 1], label="Prevalent Hypertension (0=No, 1=Yes)")
+            totChol = gr.Slider(100, 400, step=1, label="Total Cholesterol in mg/dl")
+            diaBP = gr.Slider(60, 120, step=1, label="Diastolic/Higher BP")
+            heartRate = gr.Slider(50, 120, step=1, label="Heart Rate")
+        
+        with gr.Column():
+            age = gr.Slider(20, 80, step=1, label="Age (years)")
+            BPMeds = gr.Radio([0, 1], label="On BP Medications (0=No, 1=Yes)")
+            diabetes = gr.Radio([0, 1], label="Diabetes (0=No, 1=Yes)")
+            sysBP = gr.Slider(90, 200, step=1, label="Systolic BP/Lower BP")
+            BMI = gr.Slider(15, 40, step=0.1, label="Body Mass Index (BMI) in kg/m2")
+            glucose = gr.Slider(50, 250, step=1, label="Fasting Glucose Level")
+    
+    # Center-aligned prediction output
+    with gr.Row():
+        gr.HTML("<div style='text-align: center; width: 100%'>Heart Attack Prediction</div>")
+    
+    with gr.Row():
+        prediction_output = gr.Textbox(label="", interactive=False, elem_id="prediction_output")
+    
+    # Link inputs and prediction output
+    submit_btn = gr.Button("Submit")
+    submit_btn.click(fn=predict_heart_attack, inputs=[Gender, age, cigsPerDay, BPMeds, prevalentHyp, diabetes, totChol, sysBP, diaBP, BMI, heartRate, glucose], outputs=prediction_output)
+
+app.launch(share = True)

requirements.txt

@@ -0,0 +1,6 @@
+gradio==5.1.0
+gradio_client==1.4.0
+joblib==1.4.2
+numpy==1.26.4
+pandas
+scikit-learn==1.4.2

scaler.pkl

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+oid sha256:1247693b05d68aa6ffe3aeb3833d11bb9059977bccee21af172a81aa04e9464f
+size 941

xgb_classifier.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:09e1d72186e5696767a3a3bbb7343fe43bf76d2146ea568a21a4ce4d1339d2bf
+size 1329811