Upload health_grade_predictor.py

health_grade_predictor.py

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+"""
+Restaurant Health Grade Predictor
+----------------------------------
+A Gradio app that predicts health inspection grades (A/B/C)
+using a placeholder Random Forest model trained on synthetic data.
+
+Requirements:
+    pip install gradio scikit-learn matplotlib numpy pandas
+"""
+
+import gradio as gr
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.preprocessing import LabelEncoder
+import warnings
+
+warnings.filterwarnings("ignore")
+
+# ──────────────────────────────────────────────────────────────────────────────
+# 1. Build a placeholder Random Forest model on synthetic data
+# ──────────────────────────────────────────────────────────────────────────────
+
+CUISINE_TYPES = [
+    "American", "Chinese", "Italian", "Mexican", "Japanese",
+    "Indian", "Thai", "Mediterranean", "French", "Korean",
+]
+
+VIOLATION_CODES = [
+    "No Violation",
+    "02A - No food safety certificate",
+    "04L - Evidence of mice or rats",
+    "06C - Food not protected",
+    "08A - Facility not sanitized",
+    "10B - Plumbing not properly installed",
+    "15L - Workers not using proper hygiene",
+]
+
+GRADE_LABELS = ["A", "B", "C"]
+
+# Encode categorical features
+cuisine_enc = LabelEncoder().fit(CUISINE_TYPES)
+violation_enc = LabelEncoder().fit(VIOLATION_CODES)
+
+def encode_inputs(cuisine: str, violation: str, score: float) -> np.ndarray:
+    c = cuisine_enc.transform([cuisine])[0]
+    v = violation_enc.transform([violation])[0]
+    return np.array([[c, v, score]])
+
+
+def generate_synthetic_data(n: int = 2000, seed: int = 42) -> tuple:
+    rng = np.random.default_rng(seed)
+    cuisines  = rng.integers(0, len(CUISINE_TYPES), n)
+    violations = rng.integers(0, len(VIOLATION_CODES), n)
+    scores    = rng.uniform(0, 100, n)
+
+    # Grade logic: score drives grade; violations add noise
+    grades = []
+    for i in range(n):
+        base = scores[i]
+        penalty = violations[i] * 3          # higher code → worse grade
+        effective = base - penalty
+        if effective >= 60:
+            grades.append(0)                 # A
+        elif effective >= 40:
+            grades.append(1)                 # B
+        else:
+            grades.append(2)                 # C
+
+    X = np.column_stack([cuisines, violations, scores])
+    y = np.array(grades)
+    return X, y
+
+
+print("Training placeholder Random Forest model …")
+X_train, y_train = generate_synthetic_data()
+model = RandomForestClassifier(n_estimators=100, random_state=42, n_jobs=-1)
+model.fit(X_train, y_train)
+print("Model ready ✓")
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# 2. Prediction + chart function
+# ──────────────────────────────────────────────────────────────────────────────
+
+GRADE_COLORS = {
+    "A": "#2ECC71",   # green
+    "B": "#F39C12",   # amber
+    "C": "#E74C3C",   # red
+}
+
+def predict_grade(cuisine: str, violation: str, score: float):
+    """Run inference and return a grade label and a probability bar chart."""
+    X = encode_inputs(cuisine, violation, score)
+    proba = model.predict_proba(X)[0]               # shape (3,)
+    pred_idx = int(np.argmax(proba))
+    grade = GRADE_LABELS[pred_idx]
+    confidence = proba[pred_idx] * 100
+
+    # ── build the bar chart ──────────────────────────────────────────────────
+    fig, ax = plt.subplots(figsize=(6, 3.5))
+    fig.patch.set_facecolor("#1A1A2E")
+    ax.set_facecolor("#16213E")
+
+    bar_colors = [GRADE_COLORS[g] for g in GRADE_LABELS]
+    bars = ax.bar(
+        GRADE_LABELS,
+        proba * 100,
+        color=bar_colors,
+        width=0.5,
+        edgecolor="none",
+        zorder=3,
+    )
+
+    # highlight the predicted grade with a glow border
+    pred_bar = bars[pred_idx]
+    pred_bar.set_linewidth(2.5)
+    pred_bar.set_edgecolor("white")
+
+    # value labels on bars
+    for bar, p in zip(bars, proba * 100):
+        ax.text(
+            bar.get_x() + bar.get_width() / 2,
+            bar.get_height() + 1.5,
+            f"{p:.1f}%",
+            ha="center", va="bottom",
+            color="white", fontsize=11, fontweight="bold",
+        )
+
+    ax.set_ylim(0, 110)
+    ax.set_xlabel("Predicted Grade", color="#AAAACC", fontsize=11, labelpad=8)
+    ax.set_ylabel("Probability (%)", color="#AAAACC", fontsize=11, labelpad=8)
+    ax.set_title(
+        f"Model Confidence — Predicted Grade: {grade}  ({confidence:.1f}%)",
+        color="white", fontsize=13, fontweight="bold", pad=12,
+    )
+    ax.tick_params(colors="white", labelsize=12)
+    for spine in ax.spines.values():
+        spine.set_visible(False)
+    ax.yaxis.grid(True, color="#2A2A4A", linewidth=0.8, zorder=0)
+    ax.set_axisbelow(True)
+
+    plt.tight_layout()
+
+    # ── compose the text output ───────────────────────────────────────────────
+    emoji = {"A": "🟢", "B": "🟡", "C": "🔴"}[grade]
+    summary = (
+        f"{emoji}  Predicted Health Grade: **{grade}**\n\n"
+        f"Confidence: {confidence:.1f}%\n\n"
+        f"---\n"
+        f"| Input | Value |\n"
+        f"|---|---|\n"
+        f"| Cuisine | {cuisine} |\n"
+        f"| Violation | {violation} |\n"
+        f"| Inspection Score | {score:.1f} |\n\n"
+        f"*Note: This uses a placeholder Random Forest model trained on "
+        f"synthetic data. Replace `generate_synthetic_data()` and re-train "
+        f"with real inspection records for production use.*"
+    )
+
+    return summary, fig
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# 3. Gradio UI
+# ──────────────────────────────────────────────────────────────────────────────
+
+DESCRIPTION = """
+## 🍽️ Restaurant Health Grade Predictor
+
+Enter inspection details below to get a predicted **A / B / C** health grade
+and a probability breakdown from the Random Forest model.
+"""
+
+with gr.Blocks(
+    title="Health Grade Predictor",
+    theme=gr.themes.Soft(
+        primary_hue="violet",
+        secondary_hue="slate",
+        neutral_hue="slate",
+    ),
+    css="""
+        .predict-btn { font-size: 1.1rem !important; padding: 0.7rem !important; }
+        #grade-output .prose { font-size: 1.05rem !important; }
+    """,
+) as demo:
+
+    gr.Markdown(DESCRIPTION)
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            cuisine_input = gr.Dropdown(
+                choices=CUISINE_TYPES,
+                value="American",
+                label="🍜 Cuisine Type",
+            )
+            violation_input = gr.Dropdown(
+                choices=VIOLATION_CODES,
+                value="No Violation",
+                label="⚠️ Violation Code",
+            )
+            score_input = gr.Slider(
+                minimum=0,
+                maximum=100,
+                value=85,
+                step=0.5,
+                label="📊 Inspection Score  (0 = worst, 100 = best)",
+            )
+            predict_btn = gr.Button(
+                "🔍  Predict Grade",
+                variant="primary",
+                elem_classes="predict-btn",
+            )
+
+        with gr.Column(scale=2):
+            grade_output = gr.Markdown(
+                value="*Fill in the inputs and click **Predict Grade**.*",
+                elem_id="grade-output",
+            )
+            chart_output = gr.Plot(label="Grade Probability Distribution")
+
+    predict_btn.click(
+        fn=predict_grade,
+        inputs=[cuisine_input, violation_input, score_input],
+        outputs=[grade_output, chart_output],
+    )
+
+    gr.Examples(
+        examples=[
+            ["Italian",      "No Violation",                   95],
+            ["Chinese",      "04L - Evidence of mice or rats", 55],
+            ["Mexican",      "08A - Facility not sanitized",   40],
+            ["Japanese",     "02A - No food safety certificate",72],
+            ["Mediterranean","15L - Workers not using proper hygiene", 30],
+        ],
+        inputs=[cuisine_input, violation_input, score_input],
+        outputs=[grade_output, chart_output],
+        fn=predict_grade,
+        cache_examples=True,
+        label="📌 Quick Examples",
+    )
+
+    gr.Markdown(
+        """
+        ---
+        **How grades work (synthetic rules used for training)**  
+        `Effective Score = Inspection Score − (Violation Code Index × 3)`  
+        • **A** → Effective ≥ 60 &nbsp;|&nbsp; **B** → 40–59 &nbsp;|&nbsp; **C** → < 40  
+
+        Replace `generate_synthetic_data()` with a real labelled dataset to make this production-ready.
+        """
+    )
+
+if __name__ == "__main__":
+    demo.launch(share=False)