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Dockerfile

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+# Use Python 3.10 as the base image
+FROM python:3.10
+
+# Set working directory
+WORKDIR /app
+
+# Copy all files to the container
+COPY . /app
+
+# Install required packages
+RUN pip install -r requirements.txt
+
+# Expose port 7860
+EXPOSE 7860
+
+# Run the Flask app
+CMD ["python", "server.py"]

requirements.txt

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+absl-py==2.1.0
+alembic==1.13.3
+asttokens==2.4.1
+astunparse==1.6.3
+blinker==1.8.2
+certifi==2024.8.30
+charset-normalizer==3.3.2
+Choco==1.0.5
+click==8.1.7
+cloudpickle==3.1.0
+colorama==0.4.6
+colorlog==6.8.2
+comm==0.2.2
+contourpy==1.3.0
+cycler==0.12.1
+debugpy==1.8.6
+decorator==5.1.1
+et_xmlfile==2.0.0
+executing==2.1.0
+Flask==3.0.3
+Flask-Cors==5.0.0
+flatbuffers==24.3.25
+fonttools==4.54.1
+gast==0.6.0
+google-pasta==0.2.0
+gower==0.1.2
+greenlet==3.1.1
+grpcio==1.66.2
+h5py==3.12.1
+idna==3.10
+ipykernel==6.29.5
+ipython==8.28.0
+itsdangerous==2.2.0
+jedi==0.19.1
+Jinja2==3.1.4
+joblib==1.4.2
+jupyter_client==8.6.3
+jupyter_core==5.7.2
+keras==3.6.0
+kiwisolver==1.4.7
+libclang==18.1.1
+llvmlite==0.43.0
+Mako==1.3.5
+Markdown==3.7
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+matplotlib==3.9.2
+matplotlib-inline==0.1.7
+mdurl==0.1.2
+ml-dtypes==0.4.1
+namex==0.0.8
+nest-asyncio==1.6.0
+numba==0.60.0
+numpy==1.26.4
+openpyxl==3.1.5
+opt_einsum==3.4.0
+optree==0.13.0
+optuna==4.0.0
+packaging==24.1
+pandas==2.2.3
+parso==0.8.4
+pillow==10.4.0
+platformdirs==4.3.6
+prompt_toolkit==3.0.48
+protobuf==4.25.5
+psutil==6.0.0
+pure_eval==0.2.3
+Pygments==2.18.0
+pyparsing==3.1.4
+python-dateutil==2.9.0.post0
+pytz==2024.2
+PyYAML==6.0.2
+pyzmq==26.2.0
+requests==2.32.3
+rich==13.9.2
+scikit-learn==1.5.2
+scipy==1.14.1
+seaborn==0.13.2
+setuptools==75.1.0
+shap==0.46.0
+six==1.16.0
+slicer==0.0.8
+SQLAlchemy==2.0.36
+stack-data==0.6.3
+tensorboard==2.17.1
+tensorboard-data-server==0.7.2
+tensorflow==2.17.0
+tensorflow-intel==2.17.0
+termcolor==2.4.0
+threadpoolctl==3.5.0
+tornado==6.4.1
+tqdm==4.66.5
+traitlets==5.14.3
+typing_extensions==4.12.2
+tzdata==2024.2
+urllib3==2.2.3
+wcwidth==0.2.13
+Werkzeug==3.0.4
+wheel==0.44.0
+wrapt==1.16.0
+xgboost==2.1.1

rf_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:41b44ae921d87879451237b3195675f3c475cbd1fd904c734c12f16bd0a0be1f
+size 1935849

server.py

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+from flask import Flask, request, render_template
+import pandas as pd
+import joblib
+
+app = Flask(__name__)
+
+# Load your pre-trained model
+model = joblib.load('rf_model.pkl')
+threshold = 0.7032966204148201
+
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    try:
+        # Extract values from the form
+        features = [
+            float(request.form.get('AppendixDiameter')),
+            float(request.form.get('ReboundTenderness')),
+            float(request.form.get('CoughingPain')),
+            float(request.form.get('FreeFluids')),
+            float(request.form.get('MigratoryPain')),
+            float(request.form.get('BodyTemp')),
+            float(request.form.get('KetonesInUrine')),
+            float(request.form.get('Nausea')),
+            float(request.form.get('WBCCount')),
+            float(request.form.get('NeutrophilPerc')),
+            float(request.form.get('CRPEntry')),
+            float(request.form.get('Peritonitis'))
+        ]
+        print("features",features)
+    except Exception as e:
+        return render_template('index.html', prediction_text="Error in input: " + str(e))
+    
+    # Create a DataFrame with proper column names
+    columns = ['AppendixDiameter', 'ReboundTenderness', 'CoughingPain', 'FreeFluids', 'MigratoryPain',
+               'BodyTemp', 'KetonesInUrine', 'Nausea', 'WBCCount', 'NeutrophilPerc', 'CRPEntry', 'Peritonitis']
+    data = pd.DataFrame([features], columns=columns)
+    
+    # Get the predicted probability and apply your threshold
+    pred_prob = model.predict_proba(data)[0][1]
+    prediction = 1 if pred_prob >= threshold else 0
+    
+    result = "Appendicitis" if prediction == 1 else "No Appendicitis"
+    
+    return render_template('index.html', prediction_text=f"Prediction: {result}")
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860)