chore(): nitial commit - project structure

.cz.toml

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+[tool.commitizen]
+name = "cz_customize"
+tag_format = "v$version"
+version = "0.1.0"
+version_scheme = "semver"
+update_changelog_on_bump = true
+major_version_zero = true
+
+[tool.commitizen.customize]
+message_template = "{{type}}({{scope}}): {{message}}"
+questions = [
+    { name = "type", type = "list", message = "Select the type of change you are committing", choices = [
+        { value = "feat", name = "feat: A new feature. Correlates with MINOR in SemVer" },
+        { value = "fix", name = "fix: A bug fix. Correlates with PATCH in SemVer" },
+        { value = "docs", name = "docs: Documentation only changes" },
+        { value = "style", name = "style: Changes that do not affect the meaning of the code" },
+        { value = "refactor", name = "refactor: A code change that neither fixes a bug nor adds a feature" },
+        { value = "perf", name = "perf: A code change that improves performance" },
+        { value = "test", name = "test: Adding or correcting tests" },
+        { value = "build", name = "build: Changes that affect the build system or dependencies" },
+        { value = "ci", name = "ci: Changes to CI/CD configuration" },
+        { value = "chore", name = "chore: Routine tasks like config or meta updates" }
+    ] },
+    { name = "scope", type = "input", message = "What is the scope of this change (e.g. component or file name):" },
+    { name = "message", type = "input", message = "Write a short, imperative tense description of the change:" },
+    { name = "body", type = "input", message = "Provide a longer description of the change (optional):" },
+    { name = "is_breaking_change", type = "confirm", message = "Are there any breaking changes?" },
+    { name = "breaking_change", type = "input", message = "Describe the breaking changes:" }
+]

app/handler.py

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+from models.base import BaseModel
+
+# Exemple de modèle minimal
+class DummyModel(BaseModel):
+    def predict(self, text):
+        if "stupide" in text.lower():
+            return "toxique"
+        return "non-toxique"
+
+dummy = DummyModel()
+
+def predict(text: str) -> str:
+    return dummy.predict(text)

app/interface.py

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+import gradio as gr
+from app.handler import predict
+
+def launch_app():
+    iface = gr.Interface(
+        fn=predict,
+        inputs="text",
+        outputs="label",
+        title="🧪 ToxiCheck",
+        description="Entrez un texte pour détecter s'il est toxique."
+    )
+    iface.launch()

main.py

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+from app.interface import launch_app
+
+if __name__ == '__main__':
+    launch_app()

models/base.py

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+from abc import ABC, abstractmethod
+
+class BaseModel(ABC):
+    @abstractmethod
+    def predict(self, text: str) -> str:
+        pass

requirements.txt

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+gradio
+transformers
+scikit-learn
+pandas
+pytest
+commitizen

tests/test_handler.py

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+from app.handler import predict
+
+def test_prediction_output():
+    result = predict("Tu es stupide")
+    assert result in ["toxique", "non-toxique"]