app.py

import gradio as gr
from gradio_multimodalchatbot import MultimodalChatbot
from gradio.data_classes import FileData
import os
import pandas as pd
import requests
from PIL import Image, UnidentifiedImageError
from io import BytesIO
import matplotlib.pyplot as plt
import urllib3
from transformers import pipeline
from transformers import BitsAndBytesConfig
import torch
import textwrap
import pandas as pd
import numpy as np
from haversine import haversine  # Install haversine library: pip install haversine
from transformers import AutoProcessor, LlavaForConditionalGeneration
from transformers import BitsAndBytesConfig
import torch
from huggingface_hub import InferenceClient
from transformers import AutoTokenizer
from transformers import AutoImageProcessor
from datasets import load_dataset
from geopy.geocoders import Nominatim
import pyarrow
import spaces 
IS_SPACES_ZERO = os.environ.get("SPACES_ZERO_GPU", "0") == "1"
IS_SPACE = os.environ.get("SPACE_ID", None) is not None

# Constants
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
LOW_MEMORY = os.getenv("LOW_MEMORY", "0") == "1"
MODEL_ID = "llava-hf/llava-1.5-7b-hf"
TEXT_MODEL_ID = "mistralai/Mistral-7B-Instruct-v0.2"

# Print device and memory info
print(f"Using device: {DEVICE}")
print(f"Low memory: {LOW_MEMORY}")

# Quantization configuration for efficient model loading
# Define BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16
)

# Load the tokenizer associated with your 'MODEL_ID'
tokenizer_image_to_text = AutoTokenizer.from_pretrained(MODEL_ID)
# Load the image processor associated with your 'MODEL_ID'
image_processor = AutoImageProcessor.from_pretrained(MODEL_ID) 
# Load models only once
processor = AutoProcessor.from_pretrained(MODEL_ID)
model = LlavaForConditionalGeneration.from_pretrained(MODEL_ID, quantization_config=quantization_config, device_map="auto")
# Pass the tokenizer, image processor explicitly to the pipeline
pipe_image_to_text = pipeline("image-to-text", model=model, tokenizer=tokenizer_image_to_text, image_processor=image_processor, model_kwargs={"quantization_config": quantization_config})
# Initialize the text generation pipeline

pipe_text = pipeline(
    "text-generation", 
    model=TEXT_MODEL_ID, 
    model_kwargs={
        "quantization_config": quantization_config,
        "use_auth_token": True  # This will use the environment variable
    }
)
# Ensure data files are available
current_directory = os.getcwd()
geocoded_hotels_path = os.path.join(current_directory, 'geocoded_hotels.csv')
csv_file_path = os.path.join(current_directory, 'hotel_multimodal.csv')

# Load geocoded hotels data
if not os.path.isfile(geocoded_hotels_path):
    url = 'https://github.com/ruslanmv/watsonx-with-multimodal-llava/raw/master/geocoded_hotels.csv'
    response = requests.get(url)
    if response.status_code == 200:
        with open(geocoded_hotels_path, 'wb') as f:
            f.write(response.content)
        print(f"File {geocoded_hotels_path} downloaded successfully!")
    else:
        print(f"Error downloading file. Status code: {response.status_code}")
else:
    print(f"File {geocoded_hotels_path} already exists.")
geocoded_hotels = pd.read_csv(geocoded_hotels_path)

# Load hotel dataset
if not os.path.exists(csv_file_path):
    dataset = load_dataset("ruslanmv/hotel-multimodal")
    df_hotels = dataset['train'].to_pandas()
    df_hotels.to_csv(csv_file_path, index=False)
    print("Dataset downloaded and saved as CSV.")
else:
    df_hotels = pd.read_csv(csv_file_path)

def get_current_location():
    try:
        response = requests.get('https://ipinfo.io/json')
        data = response.json()
        location = data.get('loc', '')
        if location:
            return map(float, location.split(','))
        else:
            return None, None
    except Exception as e:
        print(f"An error occurred: {e}")
        return None, None

def get_coordinates(location_name):
    geolocator = Nominatim(user_agent="coordinate_finder")
    location = geolocator.geocode(location_name)
    if location:
        return location.latitude, location.longitude
    else:
        return None

def find_nearby(place=None):
    if place:
        coordinates = get_coordinates(place)
        if coordinates:
            latitude, longitude = coordinates
            print(f"The coordinates of {place} are: Latitude: {latitude}, Longitude: {longitude}")
        else:
            print(f"Location not found: {place}")
            return None
    else:
        latitude, longitude = get_current_location()
        if not latitude or not longitude:
            print("Could not retrieve the current location.")
            return None
    
    geocoded_hotels['distance_km'] = geocoded_hotels.apply(
        lambda row: haversine((latitude, longitude), (row['latitude'], row['longitude'])),
        axis=1
    )
    
    closest_hotels = geocoded_hotels.sort_values(by='distance_km').head(5)
    print("The 5 closest locations are:\n")
    print(closest_hotels)
    return closest_hotels

# Suppress InsecureRequestWarning
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)

@spaces.GPU
# Define the respond function
def search_hotel(place=None):
    df_found = find_nearby(place)
    if df_found is None:
        return pd.DataFrame()

    df_found = df_found.head(1)  # Only last 1 hotels, to save runtime of Hugging Face ZERO GPU
    hotel_ids = df_found["hotel_id"].values.tolist()
    filtered_df = df_hotels[df_hotels['hotel_id'].isin(hotel_ids)]
    
    # Use .loc[] to avoid SettingWithCopyWarning
    filtered_df.loc[:, 'hotel_id'] = pd.Categorical(filtered_df['hotel_id'], categories=hotel_ids, ordered=True)
    filtered_df = filtered_df.sort_values('hotel_id').reset_index(drop=True)
    grouped_df = filtered_df.groupby('hotel_id', observed=True).head(1)
    description_data = []

    for index, row in grouped_df.iterrows():
        hotel_id = row['hotel_id']
        hotel_name = row['hotel_name']
        image_url = row['image_url']

        try:
            response = requests.get(image_url, verify=False)
            response.raise_for_status()
            img = Image.open(BytesIO(response.content))
            prompt = "USER: <image>\nAnalyze this image. Give me feedback on whether this hotel is worth visiting based on the picture. Provide a summary review.\nASSISTANT:"
            outputs = pipe_image_to_text(img, prompt=prompt, generate_kwargs={"max_new_tokens": 200})
            description = outputs[0]["generated_text"].split("\nASSISTANT:")[-1].strip()
            description_data.append({'hotel_name': hotel_name, 'hotel_id': hotel_id, 'image': img, 'description': description})
        except (requests.RequestException, UnidentifiedImageError):
            print(f"Skipping image at URL: {image_url}")

    return pd.DataFrame(description_data)


def show_hotels(place=None):
    description_df = search_hotel(place)
    if description_df.empty:
        print("No hotels found.")
        return
    num_images = len(description_df)
    num_rows = (num_images + 1) // 2

    fig, axs = plt.subplots(num_rows * 2, 2, figsize=(20, 10 * num_rows))

    current_index = 0
    for _, row in description_df.iterrows():
        img = row['image']
        description = row['description']

        if img is None:
            continue

        row_idx = (current_index // 2) * 2
        col_idx = current_index % 2

        axs[row_idx, col_idx].imshow(img)
        axs[row_idx, col_idx].axis('off')
        axs[row_idx, col_idx].set_title(f"{row['hotel_name']}\nHotel ID: {row['hotel_id']} Image {current_index + 1}", fontsize=16)

        wrapped_description = "\n".join(textwrap.wrap(description, width=50))
        axs[row_idx + 1, col_idx].text(0.5, 0.5, wrapped_description, ha='center', va='center', wrap=True, fontsize=14)
        axs[row_idx + 1, col_idx].axis('off')

        current_index += 1

    plt.tight_layout()
    plt.show()

def grouped_description(description_df):
    grouped_descriptions = description_df.groupby('hotel_id')['description'].apply(lambda x: ' '.join(x.astype(str))).reset_index()
    result_df = pd.merge(grouped_descriptions, description_df[['hotel_id', 'hotel_name']], on='hotel_id', how='left')
    result_df = result_df.drop_duplicates(subset='hotel_id', keep='first')
    result_df = result_df[['hotel_name', 'hotel_id', 'description']]
    return result_df

def create_prompt_result(result_df):
    prompt = ""
    for _, row in result_df.iterrows():
        hotel_name = row['hotel_name']
        hotel_id = row['hotel_id']
        description = row['description']
        prompt += f"Hotel Name: {hotel_name}\nHotel ID: {hotel_id}\nDescription: {description}\n\n"
    return prompt

def build_prompt(context_result):
    hotel_recommendation_template = """
<s>[INST] <<SYS>>
You are a helpful and informative chatbot assistant.
<</SYS>>
Based on the following hotel descriptions, recommend the best hotel:
{context_result}
[/INST]
"""
    return hotel_recommendation_template.format(context_result=context_result)
@spaces.GPU
# Define the respond function
def generate_text_response(prompt):
    outputs = pipe_text(prompt, max_new_tokens=500)
    response = outputs[0]['generated_text'].split("[/INST]")[-1].strip()
    return response







def multimodal_results(description_df):
    conversation = []
    for _, row in description_df.iterrows():
        hotel_name = row['hotel_name']
        description = row['description']
        img = row['image']

        img_path = f"{hotel_name}.png"
        img.save(img_path)

        bot_msg = {
            "text": f"Here is {hotel_name}. {description}",
            "files": [{"file": FileData(path=img_path)}]
        }

        conversation.append([{"text": "", "files": []}, bot_msg])

    return conversation

def llm_results(description_df):
    result_df = grouped_description(description_df)
    context_result = create_prompt_result(result_df)
    recommendation_prompt = build_prompt(context_result)
    result = generate_text_response(recommendation_prompt)
    conversation = [[{"text": "Based on your search...", "files": []}, {"text": f"**My recommendation:** {result}", "files": []}]]
    return conversation

def chatbot_response(user_input, conversation):
    bot_initial_message = {
        "text": f"Looking for hotels in {user_input}...",
        "files": []
    }
    conversation.append([{"text": user_input, "files": []}, bot_initial_message])
    
    yield conversation
    
    description_df = search_hotel(user_input)
    
    if description_df is None or description_df.empty:
        error_message = {"text": f"Sorry, I couldn't find any hotels for {user_input}. Please try another location.", "files": []}
        conversation.append([{"text": user_input, "files": []}, error_message])
        yield conversation
        return  # Exit the function early

    hotel_conversation = multimodal_results(description_df)
    
    for message_pair in hotel_conversation:
        conversation.append(message_pair)
        yield conversation
    
    final_recommendation = llm_results(description_df)
    for message_pair in final_recommendation:
        conversation.append(message_pair)
        yield conversation


def initial_conversation():
    return [[
           {"text": "**Welcome to Hotel Recommendation!**", "files": []},
           {"text": "Please enter the place you're interested in visiting.", "files": []}
           ]]

with gr.Blocks() as demo:
    gr.Markdown("# 🏨 Hotel Recommendation Chatbot")
    gr.Markdown("**Provide the location to discover hotels and receive personalized recommendations!**")

    initial_conv = initial_conversation()
    chatbot = MultimodalChatbot(value=initial_conv, height=500)

    with gr.Row():
        place_input = gr.Textbox(label="Enter a place", placeholder="E.g., Paris France, Tokyo Japan, Genova Italy")
        send_btn = gr.Button("Search Hotels")

    send_btn.click(chatbot_response, inputs=[place_input, chatbot], outputs=chatbot)

demo.launch(debug=True)