🚀 Initial upload of my app

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+demo/demo.mp4 filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 Eslam Tarek
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# PersonalityClassifier — Introvert vs Extrovert Predictor
+
+A lightweight Streamlit app that predicts whether a person is likely an Introvert or Extrovert from simple daily-behavior inputs.
+
+---
+
+## Table of Contents
+- **[Demo](#demo)**
+- **[Features](#features)**
+- **[Installation / Setup](#installation--setup)**
+- **[Usage](#usage)**
+- **[Configuration / Options](#configuration--options)**
+- **[Contributing](#contributing)**
+- **[License](#license)**
+- **[Acknowledgements / Credits](#acknowledgements--credits)**
+
+---
+
+## Demo
+Real demo assets found in `./demo/`:
+
+- Image: `./demo/demo.png`
+- Video: `./demo/demo.mp4`
+
+Example render:
+
+![App Screenshot](./demo/demo.png)
+
+If your viewer supports video playback in Markdown, you can also preview the short clip:
+
+```text
+./demo/demo.mp4
+```
+
+---
+
+## Features
+- **Simple UI** built with `streamlit` for quick interaction.
+- **Preprocessing utilities** in `utils.py` convert raw inputs to model-ready features.
+- **Saved model loading** via `joblib` from `./models/model.pkl`.
+- **Deterministic inference** using a binary classifier (Introvert vs Extrovert).
+
+---
+
+## Installation / Setup
+Use a Python virtual environment for isolation.
+
+```bash
+# Create a virtual environment
+python -m venv .venv
+
+# Activate it
+# On Linux/Mac:
+source .venv/bin/activate
+# On Windows:
+.venv\Scripts\activate
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
 ---
-title: PersonaMind
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
-pinned: false
-short_description: ML Model for Extrovert vs Introvert Classification
-license: mit
+
+## Usage
+Run the Streamlit app locally:
+
+```bash
+streamlit run app.py
+```
+
+App entrypoint: `app.py`
+- Loads the model using `utils.load_model("./models/model.pkl")`.
+- Renders inputs and predictions using helpers in `ui.py`.
+
+Expected project structure:
+```
+PersonalityClassifier/
+├─ app.py
+├─ ui.py
+├─ utils.py
+├─ models/
+│  └─ model.pkl
+└─ demo/
+   ├─ demo.png
+   └─ demo.mp4
+```
+
+---
+
+## Configuration / Options
+- **Model path**: `./models/model.pkl` (default in `app.py`). Replace the file if you want another trained model. Ensure the environment includes the libraries used to train/serialize it (e.g., `scikit-learn`).
+- **Caching**: `utils.load_model` uses `@st.cache_resource` to cache the loaded model across reruns.
+
 ---
 
-# Welcome to Streamlit!
+## Contributing
+Contributions are welcome! Please:
+- **Open an issue** to discuss proposed changes.
+- **Create a PR** with a clear description, small focused commits, and screenshots for UI changes.
 
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
+---
+
+## License
+This project is licensed under the **MIT License**. See the [`LICENSE`](./LICENSE) file for details.
+
+---
 
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+## Acknowledgements / Credits
+- **Streamlit** for rapid web UI development.
+- **scikit-learn** and **joblib** for model training/serialization workflows.

app.py

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+
+import streamlit as st
+from utils import load_model, preprocess_input, predict_personality
+from ui import render_header, render_input_form, render_prediction
+
+# Set page config with light theme
+st.set_page_config(page_title="Personality Predictor", layout="centered", initial_sidebar_state="auto")
+
+# Render the app header
+render_header()
+
+# Load model
+model = load_model("./models/model.pkl")
+
+# Render input form and collect user inputs
+user_input = render_input_form()
+
+# When user submits, preprocess and predict
+if user_input is not None:
+    X = preprocess_input(user_input)
+    prediction, prob = predict_personality(model, X)
+    render_prediction(prediction, prob)

demo/demo.mp4

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+size 769583

models/model.pkl

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requirements.txt

@@ -1,3 +1,4 @@
-altair
-pandas
-streamlit
+streamlit==1.38.0
+pandas==2.2.2
+joblib==1.4.2
+scikit-learn==1.4.2

ui.py

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+import streamlit as st
+
+def render_header():
+    st.title("🔮 Personality Predictor")
+    st.write("Enter your daily behavior to predict whether you are an Introvert or Extrovert.")
+
+
+def render_input_form():
+    with st.form(key='input_form'):
+        time_alone = st.slider("Hours spent alone daily", 0, 11, 4)
+        stage_fear = st.selectbox("Stage fear?", ["Yes", "No"])
+        social_events = st.slider("Social event attendance (0-10)", 0, 10, 5)
+        going_out = st.slider("Days go outside per week", 0, 7, 3)
+        drained = st.selectbox("Drained after socializing?", ["Yes", "No"])
+        friends = st.slider("Number of close friends", 0, 15, 5)
+        posts = st.slider("Social media posts per day", 0, 10, 3)
+        submit = st.form_submit_button("Predict")
+        if submit:
+            return {
+                'Time_spent_Alone': time_alone,
+                'Stage_fear': stage_fear,
+                'Social_event_attendance': social_events,
+                'Going_outside': going_out,
+                'Drained_after_socializing': drained,
+                'Friends_circle_size': friends,
+                'Post_frequency': posts
+            }
+    return None
+
+
+def render_prediction(label, probability):
+    st.subheader("Prediction Result")
+    st.write(f"**Personality**: {label}")
+    # st.write(f"**Confidence**: {probability * 100:.1f}%")
+    if label == "Introvert":
+        st.info("You are likely an Introvert. 🌱")
+    else:
+        st.success("You are likely an Extrovert. 🎉")

utils.py

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+# utils.py
+import joblib
+import pandas as pd
+import streamlit as st
+
+@st.cache_resource
+def load_model(path: str):
+    return joblib.load(path)
+
+def preprocess_input(data: dict) -> pd.DataFrame:
+    # Build a single-row DF
+    df = pd.DataFrame([data])
+
+    # Map Yes/No to 1/0
+    df['Stage_fear'] = df['Stage_fear'].map({'Yes': 1, 'No': 0})
+    df['Drained_after_socializing'] = df['Drained_after_socializing'].map({'Yes': 1, 'No': 0})
+
+
+    feature_order = [
+        'Time_spent_Alone',
+        'Stage_fear',
+        'Social_event_attendance',
+        'Going_outside',
+        'Friends_circle_size',
+        'Post_frequency'
+    ]
+
+    # Reorder and return
+    return df[feature_order]
+
+def predict_personality(model, X: pd.DataFrame):
+    # Make sure to pass a numpy array if your model expects that:
+    arr = X.values
+    prob = model.predict_proba(arr)[:, 1][0]
+    label = "Introvert" if prob > 0.5 else "Extrovert"
+    return label, prob