Delete app.py

app.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)