app.py

import gradio as gr
import pandas as pd
import numpy as np
import pickle
import os
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity

# ==========================================
# 1. SETUP & DATA LOADING
# ==========================================
# Paths for the final 10k dataset and embeddings
CSV_PATH = "cleaned_dataset_10k.csv"
PKL_PATH = "final_embeddings_10k.pkl"

if not os.path.exists(CSV_PATH) or not os.path.exists(PKL_PATH):
    raise FileNotFoundError("Missing required data files (CSV or PKL) in the root directory.")

# Load the restaurant dataset
df = pd.read_csv(CSV_PATH)
df.columns = [c.strip().lower().replace(' ', '_') for c in df.columns]

# Load pre-computed embeddings (768 dimensions)
with open(PKL_PATH, 'rb') as f:
    embedding_data = pickle.load(f)
    dataset_embeddings = embedding_data['embeddings'] if isinstance(embedding_data, dict) else embedding_data

# Load the semantic transformer model (MPNet)
# This model converts natural language queries into semantic vectors
model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')

# Pre-calculate Taste Profiles (Mean Vectors) for each of the 6 personas
persona_profiles = {}
for persona in df['reviewer_persona'].unique():
    if pd.isna(persona): continue
    indices = df[df['reviewer_persona'] == persona].index
    persona_vectors = dataset_embeddings[indices]
    persona_profiles[persona] = np.mean(persona_vectors, axis=0)

# ==========================================
# 2. HYBRID RECOMMENDATION ENGINE
# ==========================================
def run_ven_engine(budget, dietary, company, purpose, noise):
    """
    Finds the best restaurant match by combining Persona Similarity (General Taste)
    and Review Similarity (Contextual Fit).
    """
    # Create the user's specific context string
    user_context = f"Searching for a {budget} experience, {dietary} friendly. Group: {company}. Occasion: {purpose}. Atmosphere: {noise}."
    query_vec = model.encode([user_context])
    
    # Step A: Identify the closest overall Persona Profile
    persona_sims = {p: cosine_similarity(query_vec, v.reshape(1, -1))[0][0] 
                    for p, v in persona_profiles.items()}
    closest_persona = max(persona_sims, key=persona_sims.get)
    
    # Step B: Search specifically within that persona's reviews for the best contextual fit
    persona_indices = df[df['reviewer_persona'] == closest_persona].index
    persona_embeddings = dataset_embeddings[persona_indices]
    
    # Compute similarity for every individual review in this persona group
    sub_similarities = cosine_similarity(query_vec, persona_embeddings)[0]
    
    persona_df = df.loc[persona_indices].copy()
    persona_df['semantic_fit'] = sub_similarities
    persona_df['norm_rating'] = persona_df['rating_score'] / 5.0
    
    # CALCULATE HYBRID SCORE (70% context match + 30% original rating)
    persona_df['final_score'] = (persona_df['semantic_fit'] * 0.7) + (persona_df['norm_rating'] * 0.3)
    
    # Retrieve the top re-ranked result
    top_match = persona_df.sort_values(by='final_score', ascending=False).iloc[0]
    match_pct = int(top_match['final_score'] * 100)
    
    # Return Styled HTML Card (with absolute colors to prevent theme overrides)
    return f"""
    <div style="background: white; border-radius: 20px; padding: 25px; color: #0f172a !important; text-align: left; border-left: 10px solid #f97316; box-shadow: 0 10px 25px rgba(0,0,0,0.2);">
        <div style="display:flex; justify-content:space-between; align-items: flex-start;">
            <div style="flex: 1;">
                <h2 style="margin:0; font-size: 24px; font-weight: 900; color: #0f172a !important;">{top_match['restaurant_name']}</h2>
                <div style="font-size: 14px; color: #475569 !important; font-weight: 700; margin-top: 4px;">Matched for: {closest_persona} profile</div>
            </div>
            <div style="text-align:right; background: #f8fafc; padding: 10px; border-radius: 12px; border: 1px solid #e2e8f0;">
                <div style="font-size: 30px; font-weight: 950; color: #2563eb !important; line-height: 1;">{top_match['rating_score']}</div>
                <div style="font-size:10px; font-weight:900; color: #64748b !important; letter-spacing: 1px; margin-top: 5px;">RATING</div>
            </div>
        </div>
        <hr style="border:0; border-top: 1px solid #e2e8f0; margin: 15px 0;">
        <p style="color: #1e293b !important; line-height:1.6; font-size: 16px; font-weight: 500;">
            <i style="color: #334155 !important;">"{top_match['review_content_clean'][:200]}..."</i>
        </p>
        <div style="margin-top:20px; display: flex; justify-content: space-between; align-items: center;">
             <span style="font-size: 13px; font-weight: 800; color: #f97316;">VEN Match Confidence: {match_pct}%</span>
             <span style="font-size: 11px; background: #0f172a; color: white; padding: 4px 10px; border-radius: 6px; font-weight: 700;">AI MATCH</span>
        </div>
    </div>
    """

# ==========================================
# 3. GRADIO UI SETUP (VISIBILITY FIX)
# ==========================================
# We use targeted CSS to ensure labels are white on dark background, 
# but choices inside radio buttons are dark on light background.
ven_css = """
/* Global Container */
.gradio-container { background-color: #0f172a !important; }
h1 { color: white !important; text-align: center; font-weight: 950 !important; font-size: 2.5rem !important; margin-bottom: 20px !important; }

/* Input Header Labels (The questions above the inputs) */
label span { 
    color: white !important; 
    font-weight: 700 !important; 
    font-size: 14px !important; 
    margin-bottom: 5px !important;
}

/* RADIO CHOICE TEXT (The actual options like 'Solo' or 'Lively') */
/* These elements are usually inside white/grey boxes in Gradio theme, 
   so we force the text to be DARK for visibility. */
.gr-radio label span, 
.gr-radio span,
[data-testid="block-info"] + div label span {
    color: #1e293b !important; 
    font-weight: 700 !important;
}

/* Style the primary orange button */
.ven-button { 
    background-color: #f97316 !important; 
    color: white !important; 
    border: none !important; 
    font-weight: 900 !important; 
    font-size: 18px !important; 
    height: 52px !important; 
    border-radius: 12px !important;
}

/* Quick Vibe Starters table styling */
.gr-samples-table { background-color: #1e293b !important; color: white !important; }
"""

with gr.Blocks(css=ven_css, title="VEN - AI Restaurant Matchmaker") as demo:
    gr.Markdown("# 🍔 VEN: Restaurant Matchmaker")
    
    with gr.Row():
        with gr.Column(scale=1):
            with gr.Group():
                # Survey inputs for the 5 dimensions
                in_budget = gr.Dropdown(["Budget-friendly", "Mid-range", "Premium"], label="1. Wallet Size", value="Mid-range")
                in_diet = gr.Dropdown(["Anything", "Vegetarian", "Vegan", "Meat-lover"], label="2. Diet Preference", value="Anything")
                in_company = gr.Radio(["Solo", "Date/Couple", "Friends", "Business"], label="3. Social Context", value="Date/Couple")
                in_purpose = gr.Dropdown(["Casual dinner", "Special occasion", "Quick bite", "Professional meeting"], label="4. The Mission", value="Casual dinner")
                in_noise = gr.Radio(["Quiet/Intimate", "Moderate/Social", "Lively/Music"], label="5. Vibe / Noise", value="Moderate/Social")
                
                btn = gr.Button("Find My Table", variant="primary", elem_classes="ven-button")
        
        with gr.Column(scale=1):
            # Placeholder for the AI recommendation card
            output_ui = gr.HTML("<div style='text-align:center; padding:100px; color:#64748b; font-weight:700; border: 2px dashed #1e293b; border-radius: 20px;'>Fill the survey to generate your AI match</div>")

    gr.Markdown("### 🚀 Quick Vibe Starters")
    gr.Examples(
        examples=[
            ["Budget-friendly", "Vegetarian", "Friends", "Quick bite", "Moderate/Social"],
            ["Premium", "Meat-lover", "Date/Couple", "Special occasion", "Quiet/Intimate"],
            ["Mid-range", "Anything", "Business", "Professional meeting", "Quiet/Intimate"]
        ],
        inputs=[in_budget, in_diet, in_company, in_purpose, in_noise],
        outputs=output_ui,
        fn=run_ven_engine,
        cache_examples=False,
    )
    
    # Event binding
    btn.click(run_ven_engine, inputs=[in_budget, in_diet, in_company, in_purpose, in_noise], outputs=output_ui)

if __name__ == "__main__":
    demo.launch()