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README.md

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 ---
-title: Iris Classifier Cicd
-emoji: 📚
-colorFrom: blue
+title: Iris Classifier (CI/CD Demo)
+emoji: 🌸
+colorFrom: pink
 colorTo: purple
 sdk: gradio
-sdk_version: 6.5.1
+sdk_version: 5.29.0
 app_file: app.py
 pinned: false
+license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Iris Classifier (CI/CD Demo)
+
+A simple Iris flower classifier demonstrating automated ML deployment via GitHub Actions.
+
+## CI/CD Pipeline
+
+This model is automatically:
+1. **Validated** - Data quality checks
+2. **Tested** - Unit tests for training code
+3. **Trained** - Model training with accuracy threshold
+4. **Deployed** - Pushed to this HuggingFace Space
+
+## Model Details
+
+- **Algorithm:** Random Forest (100 trees)
+- **Dataset:** Iris (150 samples, 3 classes)
+- **Accuracy:** 90.0%
+
+## Links
+
+- [GitHub Repository](https://github.com/Algo-nav/ml-pipeline-demo)
+- [Author: Nav772](https://huggingface.co/Nav772)

app.py

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+import gradio as gr
+import pickle
+import numpy as np
+import json
+
+with open("model.pkl", "rb") as f:
+    model = pickle.load(f)
+
+with open("metrics.json", "r") as f:
+    metrics = json.load(f)
+
+CLASS_NAMES = ["Setosa", "Versicolor", "Virginica"]
+
+
+def predict(sepal_length, sepal_width, petal_length, petal_width):
+    features = np.array([[sepal_length, sepal_width, petal_length, petal_width]])
+    probabilities = model.predict_proba(features)[0]
+    result = {CLASS_NAMES[i]: float(prob) for i, prob in enumerate(probabilities)}
+    return result
+
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Slider(4.0, 8.0, value=5.8, label="Sepal Length (cm)"),
+        gr.Slider(2.0, 4.5, value=3.0, label="Sepal Width (cm)"),
+        gr.Slider(1.0, 7.0, value=4.0, label="Petal Length (cm)"),
+        gr.Slider(0.1, 2.5, value=1.2, label="Petal Width (cm)"),
+    ],
+    outputs=gr.Label(num_top_classes=3, label="Prediction"),
+    title="Iris Classifier (CI/CD Demo)",
+    description=(
+        "Classify Iris flowers based on sepal and petal measurements. "
+        "Model Performance: 90.0% accuracy. "
+        "This model was automatically trained and deployed via GitHub Actions CI/CD."
+    ),
+    examples=[
+        [5.1, 3.5, 1.4, 0.2],
+        [6.2, 2.9, 4.3, 1.3],
+        [7.7, 3.0, 6.1, 2.3],
+    ]
+)
+
+if __name__ == "__main__":
+    demo.launch()

metrics.json

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+{
+  "accuracy": 0.9,
+  "classification_report": {
+    "setosa": {
+      "precision": 1.0,
+      "recall": 1.0,
+      "f1-score": 1.0,
+      "support": 10.0
+    },
+    "versicolor": {
+      "precision": 0.8181818181818182,
+      "recall": 0.9,
+      "f1-score": 0.8571428571428571,
+      "support": 10.0
+    },
+    "virginica": {
+      "precision": 0.8888888888888888,
+      "recall": 0.8,
+      "f1-score": 0.8421052631578947,
+      "support": 10.0
+    },
+    "accuracy": 0.9,
+    "macro avg": {
+      "precision": 0.9023569023569024,
+      "recall": 0.9,
+      "f1-score": 0.899749373433584,
+      "support": 30.0
+    },
+    "weighted avg": {
+      "precision": 0.9023569023569024,
+      "recall": 0.9,
+      "f1-score": 0.8997493734335839,
+      "support": 30.0
+    }
+  },
+  "parameters": {
+    "n_estimators": 100,
+    "test_size": 0.2,
+    "random_state": 42
+  },
+  "timestamp": "2026-02-03T14:49:10.698036"
+}

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0fb1ecd200ca029ac4bd7ea3d59b5685440a85dcc01f4a3a7efabc0782d0be18
+size 158032

requirements.txt

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+gradio
+numpy
+scikit-learn