app.py

import gradio as gr
from datasets import load_dataset
from sentence_transformers import SentenceTransformer, util
import faiss
import numpy as np
from transformers import pipeline
import time
import ast
import re

# --- 1. DATA LOADING AND INITIALIZATION ---
print("===== Application Startup =====")
start_time = time.time()

# Load the travel dataset and limit to the first 20,000 rows (same as index)
print("Loading TravelPlanner dataset...")
dataset = load_dataset("osunlp/TravelPlanner", "test")
print("Dataset ready.")

# --- 2. EMBEDDING AND RECOMMENDATION ENGINE ---
print("Loading embedding model...")
model_name = "all-mpnet-base-v2"
embedding_model = SentenceTransformer(f"sentence-transformers/{model_name}")

index_file = "trip_index.faiss"

print(f"Loading FAISS index from {index_file}...")

try:
    index = faiss.read_index(index_file)
    print(f"Index is ready. Total vectors in index: {index.ntotal}")
except RuntimeError:
    print(f"Error: FAISS index file '{index_file}' not found.")
    print("Please run the `build_index.py` script first to create the index.")
    exit()


# --- 3. SYNTHETIC GENERATION ---
def format_plan_details(plan_string):
    """
    Parses and formats the raw plan string from the dataset into readable Markdown.
    """
    # If the plan is not in the expected dictionary format, return it as is.
    if not plan_string or not plan_string.strip().startswith('['):
        return plan_string

    try:
        # Safely parse the string representation of a list of dictionaries
        plan_list = ast.literal_eval(plan_string)
    except (ValueError, SyntaxError):
        # If parsing fails, return the original string to avoid crashing
        return plan_string

    formatted_sections = []
    for section in plan_list:
        description = section.get('Description', 'Details')
        content = section.get('Content', '').strip()
        
        # Add a bold title for each section
        formatted_sections.append(f"#### {description}")

        # Use specific formatting based on the section's description
        if any(keyword in description for keyword in ['Attractions', 'Restaurants', 'Accommodations', 'Flight']):
            lines = content.split('\n')
            if lines:
                # Make the header bold
                formatted_sections.append(f"**{lines[0]}**")
                # Format the rest of the lines as a clean, bulleted list
                for item in lines[1:]:
                    clean_item = ' '.join(item.split()) # Remove extra whitespace
                    if clean_item:
                        formatted_sections.append(f"- {clean_item}")
        
        elif 'Self-driving' in description or 'Taxi' in description:
            # Make simple travel descriptions more readable
            mode_emoji = "🚗" if 'Self-driving' in description else "🚕"
            formatted_sections.append(f"- {mode_emoji} {content.replace(', ', ', ')}")
        
        else:
            # Default formatting for any other type of content
            formatted_sections.append(content)
            
        # Add a newline for spacing between sections
        formatted_sections.append("") 

    return "\n".join(formatted_sections)

def get_recommendations_and_generate(query_text, k=3):
    # 1. Get Recommendations from existing data
    query_vector = embedding_model.encode([query_text])
    query_vector = np.array(query_vector, dtype=np.float32)
    distances, indices = index.search(query_vector, k)
    
    results = []
    for idx_numpy in indices[0]:
        idx = int(idx_numpy)
        trip_plan = {
            "dest": dataset['test']['dest'][idx],
            "days": dataset['test']['days'][idx],
            "reference_information": dataset['test']['reference_information'][idx]
        }
        results.append(trip_plan)
        
    while len(results) < 3:
        results.append({"dest": "No trip plan found", "days":"", "reference_information": ""})
        
    # 2. Create a prompt for the generative model
    prompt = f"Write a complete travel plan that includes a title and a day-by-day itinerary. The trip must be about: {query_text}."
    print("Loading generative model...")
    generator = pipeline('text-generation', model='gpt2')
    
    # 3. Generate 10 new, creative trip ideas
    print("Generating 10 synthetic trip ideas...")
    generated_outputs = generator(
        prompt, 
        max_new_tokens=250,  # Increased tokens for more detailed plans
        num_return_sequences=10, 
        pad_token_id=50256
    )
    
    # 4. Find the best trip out of the 10 generated
    print("Finding the most relevant generated trip...")
    generated_texts = [output['generated_text'].replace(prompt, "").strip() for output in generated_outputs]
    
    # Embed all 10 generated texts
    generated_embeddings = embedding_model.encode(generated_texts)
    
    # Calculate cosine similarity between the user's query and each generated text
    similarities = util.cos_sim(query_vector, generated_embeddings)
    
    # Find the index of the most similar generated trip
    best_recipe_index = np.argmax(similarities)
    best_generated_trip = generated_texts[best_recipe_index]

    return results[0], results[1], results[2], best_generated_trip

# --- 4. GRADIO USER INTERFACE ---
def format_trip_plan(trip):
    # Formats the recommended trips with markdown
    if not trip or 'reference_information' not in trip:
        return "### No similar trip plan found."
    formatted_plan = format_plan_details(trip['reference_information'])
    return f"### {trip['days']}-days trip to {trip['dest'].upper()}\n**Suggested Plan:**\n{formatted_plan}"

def format_generated_trip(trip_text):
    return trip_text

def trip_planner_wizard(destination, days):
    # Combine user inputs into a single query for processing
    days = int(days) # Ensure days is an integer for the f-string
    query_text = f"a {days}-day trip to {destination}"
    rec1, rec2, rec3, gen_rec_text = get_recommendations_and_generate(query_text)
    return format_trip_plan(rec1), format_trip_plan(rec2), format_trip_plan(rec3), format_generated_trip(gen_rec_text)

end_time = time.time()
print(f"Models and data loaded in {end_time - start_time:.2f} seconds.")

# Gradio Interface
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# ✈️ TripPlanner AI")
    gr.Markdown("Enter your destination and desired trip length, and get plan recommendations plus a new AI-generated idea!")
    
    with gr.Row():
        destination_input = gr.Textbox(label="Destination", placeholder="e.g., Paris")
        days_input = gr.Number(label="Number of Days", value=3)
        
    with gr.Row():
        submit_btn = gr.Button("Get Trip Plans", variant="primary")

    with gr.Row():
        with gr.Column(scale=2):
            gr.Markdown("### Recommended Trip Plans from Dataset")
            output_rec1 = gr.Markdown()
            output_rec2 = gr.Markdown()
            output_rec3 = gr.Markdown()
        with gr.Column(scale=1):
            gr.Markdown("### ✨ New AI-Generated Idea")
            output_gen = gr.Textbox(label="AI Generated Trip Plan", lines=20, interactive=False)

    submit_btn.click(
        fn=trip_planner_wizard,
        inputs=[destination_input, days_input],
        outputs=[output_rec1, output_rec2, output_rec3, output_gen]
    )
    
    gr.Examples(
        examples=[
            ["Paris", 3],
            ["Orlando", 7],
            ["Tokyo", 5],
            ["the Greek Islands", 10]
        ],
        inputs=[destination_input, days_input]
    )

demo.launch(ssr_mode=False)