backend.py

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, BitsAndBytesConfig
import torch
import textwrap
from haversine import haversine
from geopy.geocoders import Nominatim
from huggingface_hub import InferenceClient
import os
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
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16
)

# Load models only once
processor = AutoProcessor.from_pretrained(MODEL_ID)
model = LlavaForConditionalGeneration.from_pretrained(MODEL_ID, quantization_config=quantization_config, device_map="auto").to(DEVICE)
pipe_image_to_text = pipeline("image-to-text", model=model, 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})

# 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

@spaces.GPU
# Define the respond function
def search_hotel(place=None):
    df_found = find_nearby(place)
    if df_found is None:
        return pd.DataFrame()
    hotel_ids = df_found["hotel_id"].values.tolist()
    filtered_df = df_hotels[df_hotels['hotel_id'].isin(hotel_ids)]
    filtered_df['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(2)
    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