merge: resolve conflicts, keep HF Space config + v2 content

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
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Dockerfile

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+FROM python:3.13.5-slim
+WORKDIR /app
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+COPY requirements.txt ./
+RUN pip3 install -r requirements.txt
+COPY . .
+EXPOSE 8501
+HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+ENTRYPOINT ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]

README.md

@@ -1,3 +1,18 @@
+<<<<<<< HEAD
+---
+title: Project Mizan
+emoji: 🚀
+colorFrom: red
+colorTo: red
+sdk: docker
+app_port: 8501
+tags:
+- streamlit
+pinned: false
+short_description: Arabic Misinformation Detection Model
+license: mit
+---
+
 # Project Mizan ميزان: A Multilingual Misinformation Detector
 
 ## Manifesto
@@ -67,27 +82,27 @@ The fine-tuned AraBERT model is publicly available on Hugging Face Hub:
 ## How to Run Locally
 
 1. **Clone the repo**
-   ```bash
+```bash
    git clone https://github.com/KhaledTTarabay/Project-Mizan.git
    cd Project-Mizan
-   ```
+```
 
 2. **Create and activate virtual environment**
-   ```bash
+```bash
    python -m venv venv
    venv\Scripts\activate        # Windows
    source venv/bin/activate     # Mac/Linux
-   ```
+```
 
 3. **Install dependencies**
-   ```bash
+```bash
    pip install -r requirements.txt
-   ```
+```
 
 4. **Run the app**
-   ```bash
+```bash
    streamlit run app.py
-   ```
+```
 
 > Note: No local model training required. The AraBERT model loads automatically from Hugging Face Hub on first run.
 
@@ -99,8 +114,9 @@ The fine-tuned AraBERT model is publicly available on Hugging Face Hub:
 |---|---|---|
 | v1 | Deprecated | TF-IDF/KNN baseline |
 | v2 | Current | AraBERT Arabic pipeline |
-| v3 | Planned | English transformer upgrade,|
+| v3 | Planned | English transformer upgrade, morphology-native Arabic NLP |
 
 ---
 
-*This README was drafted with AI assistance.*
+*This README was drafted with AI assistance.*
+>>>>>>> 684d5eda2f4a4ae878fc998a21f04ae550ced9fa

src/streamlit_app.py

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+import altair as alt
+import numpy as np
+import pandas as pd
+import streamlit as st
+
+"""
+# Welcome to Streamlit!
+
+Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
+If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
+forums](https://discuss.streamlit.io).
+
+In the meantime, below is an example of what you can do with just a few lines of code:
+"""
+
+num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
+num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
+
+indices = np.linspace(0, 1, num_points)
+theta = 2 * np.pi * num_turns * indices
+radius = indices
+
+x = radius * np.cos(theta)
+y = radius * np.sin(theta)
+
+df = pd.DataFrame({
+    "x": x,
+    "y": y,
+    "idx": indices,
+    "rand": np.random.randn(num_points),
+})
+
+st.altair_chart(alt.Chart(df, height=700, width=700)
+    .mark_point(filled=True)
+    .encode(
+        x=alt.X("x", axis=None),
+        y=alt.Y("y", axis=None),
+        color=alt.Color("idx", legend=None, scale=alt.Scale()),
+        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
+    ))