app.py

import gradio as gr
import pandas as pd
import numpy as np
import folium
import sys
import os

# Add utils to path
sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), 'utils'))
from clean_text import clean_text
from semantic_similarity import Encoder
from ranker import compute_bayesian_popularity_score
from main import get_recommendations

print("Loading restaurant data...")
data = pd.read_csv("../data/toy_data_aggregated_embeddings.csv")
print(f"Loaded {len(data)} restaurants")

# Compute Bayesian popularity scores
print("Computing popularity scores...")
data = compute_bayesian_popularity_score(data)
print("Popularity scores computed")

print("Loading pre-computed embeddings...")
all_desc_embeddings = np.vstack(data["embedding"].values)
print(f"Loaded embeddings with shape {all_desc_embeddings.shape}")

# Initialize semantic encoder
print("Loading semantic encoder model...")
try:
    encoder = Encoder()
    print("Semantic encoder loaded")
except Exception as e:
    print(f"Warning: Could not load semantic encoder: {e}")
    print("Falling back to keyword-only search")

def create_paris_map(results_df):
    """Create interactive map of Paris restaurants"""
    paris_center = [48.8566, 2.3522]
    m = folium.Map(location=paris_center, zoom_start=12, tiles='OpenStreetMap')
    
    for idx, row in results_df.iterrows():
        lat_offset = np.random.uniform(-0.05, 0.05)
        lng_offset = np.random.uniform(-0.07, 0.07)
        coords = [48.8566 + lat_offset, 2.3522 + lng_offset]
        
        rating = float(row.get('overall_rating', 0))
        color = 'green' if rating >= 4.5 else 'blue' if rating >= 4.0 else 'orange' if rating >= 3.5 else 'red'
        
        popup_html = f"""
        <div style="width:250px">
            <h4><b>{row['name']}</b></h4>
            <p>Rating: {row.get('overall_rating', 'N/A')}</p>
            <p>Reviews: {row.get('review_count', 'N/A')}</p>
            <p>Popularity Score: {row.get('pop_score', 'N/A'):.2f}</p>
        </div>
        """
        
        folium.Marker(
            location=coords,
            popup=folium.Popup(popup_html, max_width=300),
            icon=folium.Icon(color=color, icon='cutlery', prefix='fa')
        ).add_to(m)
    
    return m._repr_html_()

# def semantic_search(query, data_source, num_results, use_popularity):
#     """Semantic search using embeddings"""
#     if not query.strip():
#         return "Please enter a search query", None
    
#     try:
#         query_clean = clean_text(query)
        
#         # Generate query embedding
#         print(f"Encoding query: {query_clean}")
#         query_embedding = encoder.encode([query_clean], show_progress_bar=False)
#         query_embedding = query_embedding.cpu().numpy()
        
#         # Compute semantic similarity
#         similarities = cosine_similarity(query_embedding, all_desc_embeddings)[0]
        
#         # Combine with popularity if requested
#         if use_popularity:
#             sim_normalized = (similarities - similarities.min()) / (similarities.max() - similarities.min() + 1e-10)
#             pop_normalized = (data["pop_score"] - data["pop_score"].min()) / (data["pop_score"].max() - data["pop_score"].min() + 1e-10)
#             # Combined score: 70% semantic, 30% popularity
#             scores = 0.7 * sim_normalized + 0.3 * pop_normalized
#         else:
#             scores = similarities
        
#         top_indices = np.argsort(scores)[-int(num_results):][::-1]
#         results = data.iloc[top_indices].copy()
#         results['similarity_score'] = scores[top_indices]
        
#         map_html = create_paris_map(results)
        
#         output = f"Found {len(results)} restaurants for '{query}'\n"
#         output += f"Data Source: {data_source}\n"
#         output += f"Search Method: Semantic Search {'+ Popularity' if use_popularity else ''}\n\n"
        
#         for idx, (_, row) in enumerate(results.iterrows(), 1):
#             name = row.get('name', 'Unknown')
#             rating = row.get('overall_rating', 'N/A')
#             reviews = row.get('review_count', 'N/A')
#             similarity = row.get('similarity_score', 0)
#             pop_score = row.get('pop_score', 0)
            
#             output += f"{idx}. **{name}**\n"
#             output += f"   Rating: {rating} | Reviews: {reviews}\n"
#             output += f"   Match: {similarity:.3f}"
#             if use_popularity:
#                 output += f" | Popularity: {pop_score:.2f}"
#             output += "\n"
            
#             if 'address' in row and pd.notna(row['address']):
#                 addr = str(row['address'])[:100]
#                 output += f"   Address: {addr}\n"
            
#             output += "\n"
        
#         return output, map_html
        
#     except Exception as e:
#         import traceback
#         return f"Error: {str(e)}\n\n{traceback.format_exc()}", None

# def keyword_search(query, data_source, num_results, use_popularity):
#     """Keyword-based search with optional popularity ranking"""
#     if not query.strip():
#         return "Please enter a search query", None
    
#     try:
#         query_clean = clean_text(query).lower()
#         query_words = set(query_clean.split())
        
#         scores = []
#         for idx, row in data.iterrows():
#             score = 0
#             name = str(row.get('name', '')).lower()
            
#             # Check name matches
#             for word in query_words:
#                 if word in name:
#                     score += 2
            
#             rating = float(row.get('overall_rating', 0))
#             score += rating * 0.5
            
#             # Add popularity if requested
#             if use_popularity:
#                 pop_score = float(row.get('pop_score', 0))
#                 score += pop_score * 0.3
            
#             scores.append(score)
        
#         top_indices = np.argsort(scores)[-int(num_results):][::-1]
#         results = data.iloc[top_indices].copy()
#         results['match_score'] = [scores[i] for i in top_indices]
        
#         map_html = create_paris_map(results)
        
#         output = f"Found {len(results)} restaurants for '{query}'\n"
#         output += f"Data Source: {data_source}\n"
#         output += f"Search Method: Keyword Search {'+ Popularity' if use_popularity else ''}\n\n"
        
#         for idx, (_, row) in enumerate(results.iterrows(), 1):
#             name = row.get('name', 'Unknown')
#             rating = row.get('overall_rating', 'N/A')
#             reviews = row.get('review_count', 'N/A')
#             match = row.get('match_score', 0)
#             pop_score = row.get('pop_score', 0)
            
#             output += f"{idx}. **{name}**\n"
#             output += f"   Rating: {rating} | Reviews: {reviews}\n"
#             output += f"   Match Score: {match:.2f}"
#             if use_popularity:
#                 output += f" | Popularity: {pop_score:.2f}"
#             output += "\n"
            
#             if 'address' in row and pd.notna(row['address']):
#                 addr = str(row['address'])[:100]
#                 output += f"   Address: {addr}\n"
            
#             output += "\n"
        
#         return output, map_html
        
#     except Exception as e:
#         import traceback
#         return f"Error: {str(e)}\n\n{traceback.format_exc()}", None

# def search_restaurants(query, data_source, search_method, num_results, use_popularity):
#     """Main search function that routes to appropriate search method"""
#     if search_method == "Semantic Search" and use_semantic:
#         return semantic_search(query, data_source, num_results, use_popularity)
#     else:
#         return keyword_search(query, data_source, num_results, use_popularity)

def search_restaurants(query_input, data_source, num_results):
    n_candidates = 100
    query_clean = clean_text(query_input)
    return get_recommendations(query_clean, n_candidates, num_results)

# Create Gradio interface
with gr.Blocks(title="Restaurant Finder", theme=gr.themes.Soft()) as app:
    gr.Markdown("""
    # Advanced Restaurant Recommendation System
    ### Search Through 5,000+ Restaurants with AI-Powered Semantic Search
    
    Find restaurants using semantic understanding and popularity ranking!
    """)
    
    with gr.Row():
        with gr.Column(scale=3):
            query_input = gr.Textbox(
                label="Search Query",
                placeholder="e.g., Italian pasta, best sushi, romantic dinner, family-friendly pizza",
                lines=2
            )
        
        with gr.Column(scale=2):
            data_source = gr.Dropdown(
                choices=["Michelin", "Google", "Yelp"],
                value="Yelp",
                label="Data Source",
                info="Select restaurant data source"
            )
    
    with gr.Row():
        # with gr.Column(scale=2):
        #     search_method = gr.Radio(
        #         choices=["Keyword Search", "Semantic Search"],
        #         value="Semantic Search" if use_semantic else "Keyword Search",
        #         label="Search Method",
        #         info="Semantic uses AI embeddings, Keyword uses exact matches"
        #     )
        
        with gr.Column(scale=1):
            num_results = gr.Slider(
                minimum=5,
                maximum=30,
                value=10,
                step=5,
                label="Results"
            )
        
        # with gr.Column(scale=1):
        #     use_popularity = gr.Checkbox(
        #         label="Use Popularity Ranking",
        #         value=True,
        #         info="Boost popular restaurants"
        #     )
    
    search_btn = gr.Button("Search Restaurants", variant="primary", size="lg")
    
    with gr.Row():
        with gr.Column(scale=1):
            results_output = gr.Textbox(
                label="Restaurant Results",
                lines=20,
                max_lines=30
            )
        
        with gr.Column(scale=1):
            map_output = gr.HTML(
                label="Paris Map"
            )
    
    gr.Markdown("### Try These Examples:")
    
    examples = [
        ["Italian pasta", "Yelp", 10],
        ["sushi", "Michelin", 10],
        ["romantic dinner", "Google", 8],
        ["family-friendly pizza", "Yelp", 10],
        ["best seafood", "Michelin", 10],
        ["cheap burger", "Google", 10]
    ]
    
    gr.Examples(
        examples=examples,
        inputs=[query_input, data_source, num_results]
    )
    
    search_btn.click(
        fn=search_restaurants,
        inputs=[query_input, data_source, num_results],
        outputs=[results_output, map_output]
    )
    
    query_input.submit(
        fn=search_restaurants,
        inputs=[query_input, data_source, num_results],
        outputs=[results_output, map_output]
    )

if __name__ == "__main__":
    print("\nStarting Advanced Restaurant Finder...")
    print(f"{len(data)} restaurants ready to search")
    print(f"Popularity Ranking: Enabled")
    print("Opening at http://127.0.0.1:7860\n")
    
    app.launch(share=False, server_name="127.0.0.1", server_port=7860, inbrowser=True)