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Dockerfile

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+FROM python:3.12-slim
+
+# Set the working directory inside the container
+WORKDIR /app
+
+# Copy all files from the current directory to the container's working directory
+COPY . .
+
+# Install dependencies from the requirements file without using cache to reduce image size
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+EXPOSE 7860
+
+
+# Define the command to start the application using Gunicorn with 4 worker processes
+# - `-w 4`: Uses 4 worker processes for handling requests
+# - `-b 0.0.0.0:7860`: Binds the server to port 7860 on all network interfaces
+# - `app:app`: Runs the Flask app (assuming `app.py` contains the Flask instance named `app`)
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "introvert_extrovert_predictor:app"]

introvert_extrovert_predictor.py

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+import joblib
+import pandas as pd
+from flask import Flask, request, jsonify
+
+# Initialize Flask app
+app = Flask("Introvert Extrovert Predictor")
+
+# Load the trained classification model
+model = joblib.load("introvert_extrovert_predictor_v1_0.joblib")
+
+# Home route
+@app.get("/")
+def home():
+    return "Welcome to the Introvert-Extrovert Prediction API!"
+
+@app.post("/v1/personality/predict")
+def predict_personality_single():
+    input_data = request.get_json()
+
+    # Prepare input sample
+    sample = {
+        "Time_spent_Alone": input_data["Time_spent_Alone"],
+        "Social_event_attendance": input_data["Social_event_attendance"],
+        "Going_outside": input_data["Going_outside"],
+        "Friends_circle_size": input_data["Friends_circle_size"],
+        "Post_frequency": input_data["Post_frequency"],
+        "Stage_fear": 1 if input_data["Stage_fear"].lower() == "yes" else 0,
+        "Drained_after_socializing": 1 if input_data["Drained_after_socializing"].lower() == "yes" else 0
+    }
+
+    # Convert to DataFrame and predict
+    input_df = pd.DataFrame([sample])
+    prediction = model.predict(input_df).tolist()[0]
+    personality = "Extrovert" if prediction == 1 else "Introvert"
+
+    return jsonify({'Predicted_Personality': personality})
+
+@app.post("/v1/personality/predictbatch")
+def predict_personality_batch():
+    # Get uploaded file
+    file = request.files['file']
+
+    # Read CSV file
+    input_df = pd.read_csv(file)
+
+    # Convert binary features to numerical (Yes → 1, No → 0)
+    input_df["Stage_fear"] = input_df["Stage_fear"].apply(lambda x: 1 if str(x).lower() == "yes" else 0)
+    input_df["Drained_after_socializing"] = input_df["Drained_after_socializing"].apply(lambda x: 1 if str(x).lower() == "yes" else 0)
+
+    # Predict personality
+    predictions = model.predict(input_df).tolist()
+    input_df["Predicted_Personality"] = ["Extrovert" if p == 1 else "Introvert" for p in predictions]
+
+    # Convert to dict for JSON output
+    result = input_df.to_dict(orient="records")
+    return jsonify(result)
+
+# Run app
+if __name__ == '__main__':
+    app.run(debug=True)

introvert_extrovert_predictor_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cdbcb530cbf3579870897c40043dec4050c5231f226c8aa8dddae457c28b162c
+size 10482322

requirements.txt

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+numpy==2.0.2
+pandas==2.2.2
+scikit-learn==1.6.1
+matplotlib==3.10.0
+seaborn==0.13.2
+joblib==1.4.2
+xgboost==2.1.4
+requests==2.32.3
+huggingface-hub==0.17.3
+flask==2.2.2
+gunicorn==20.1.0
+uvicorn[standard]==0.23.2