Initial commit

.gitignore

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+__pycache__/
+*.py[cod]
+*.so
+
+# Virtual environments & local tooling
+venv/
+.venv/
+env/
+ENV/
+.env
+.env.*
+pip-wheel-metadata/
+
+# Build / packaging artifacts
+*.egg-info/
+dist/
+build/
+
+# Notebooks & test caches
+.ipynb_checkpoints/
+.pytest_cache/
+.mypy_cache/
+.ruff_cache/
+
+# Coverage & log files
+.coverage
+coverage.xml
+htmlcov/
+*.log
+
+# OS cruft
+.DS_Store
+
+# Hugging Face cache (set HF_HOME outside repo when possible)
+.cache/huggingface/

app.py

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+import gradio as gr
+import joblib
+import pandas as pd
+from huggingface_hub import hf_hub_download   # NEW IMPORT
+
+# Features used by your model
+FEATURES = ["koi_period", "koi_duration", "koi_prad", "koi_depth"]
+
+# Download the model from Hugging Face Hub
+model_path = hf_hub_download(
+    repo_id="h0ntas/exo5",
+    filename="model.joblib" 
+)
+
+# Load the downloaded model
+model = joblib.load(model_path)
+
+
+def predict(koi_period, koi_duration, koi_prad, koi_depth):
+    # Build a one-row DataFrame with the right column names
+    X = pd.DataFrame(
+        [[koi_period, koi_duration, koi_prad, koi_depth]],
+        columns=FEATURES
+    )
+
+    # Get the predicted class (0 or 1)
+    y = model.predict(X)[0]
+
+    # Try to get predicted probability if the model supports it
+    try:
+        p = model.predict_proba(X)[0][1]
+        label = "Candidate exoplanet" if int(y) == 1 else "Likely false positive"
+        return f"{label} | probability: {p:.2f}"
+    except Exception:
+        return "Candidate exoplanet" if int(y) == 1 else "Likely false positive"
+
+
+# Gradio interface
+demo = gr.Interface(
+    fn=predict,
+    inputs=[gr.Number(label=f) for f in FEATURES],
+    outputs=gr.Textbox(label="Prediction"),
+    title="Exoplanet Transit Classifier",
+    description="Enter KOI features to get a quick classification."
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+scikit-learn
+joblib
+numpy
+pandas
+huggingface_hub