import streamlit as st
from PIL import Image
import torch
from torchvision import transforms
import pydeck as pdk
from geopy.geocoders import Nominatim
import time
import requests
from io import BytesIO
import reverse_geocoder as rg
from bs4 import BeautifulSoup
from urllib.parse import urljoin
from models.huggingface import Geolocalizer
import spacy
from collections import Counter
from spacy.cli import download
from typing import Tuple, List, Optional, Union, Dict
def load_spacy_model(model_name: str = "en_core_web_md") -> spacy.Language:
"""
Load the specified spaCy model.
Args:
model_name (str): Name of the spaCy model to load.
Returns:
spacy.Language: Loaded spaCy model.
"""
try:
return spacy.load(model_name)
except IOError:
print(f"Model {model_name} not found, downloading...")
download(model_name)
return spacy.load(model_name)
nlp = load_spacy_model()
IMAGE_SIZE = (224, 224)
GEOLOC_MODEL_NAME = "osv5m/baseline"
@st.cache_resource(show_spinner=True)
def load_geoloc_model() -> Optional[Geolocalizer]:
"""
Load the geolocation model.
Returns:
Optional[Geolocalizer]: Loaded geolocation model or None if loading fails.
"""
with st.spinner('Loading model...'):
try:
model = Geolocalizer.from_pretrained(GEOLOC_MODEL_NAME)
model.eval()
return model
except Exception as e:
st.error(f"Failed to load the model: {e}")
return None
def most_frequent_locations(text: str) -> Tuple[str, List[str]]:
"""
Find the most frequent locations mentioned in the text.
Args:
text (str): Input text to analyze.
Returns:
Tuple[str, List[str]]: Description of the most mentioned locations and a list of those locations.
"""
doc = nlp(text)
locations = []
for ent in doc.ents:
if ent.label_ in ['LOC', 'GPE']:
print(f"Entity: {ent.text} | Label: {ent.label_} | Sentence: {ent.sent}")
locations.append(ent.text)
if locations:
location_counts = Counter(locations)
most_common_locations = location_counts.most_common(2)
common_locations_str = ', '.join([f"{loc[0]} ({loc[1]} occurrences)" for loc in most_common_locations])
return f"Most Mentioned Locations: {common_locations_str}", [loc[0] for loc in most_common_locations]
else:
return "No locations found", []
def transform_image(image: Image) -> torch.Tensor:
"""
Transform the input image for model prediction.
Args:
image (Image): Input image.
Returns:
torch.Tensor: Transformed image tensor.
"""
transform = transforms.Compose([
transforms.Resize(IMAGE_SIZE),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
return transform(image).unsqueeze(0)
def check_location_match(location_query: dict, most_common_locations: List[str]) -> bool:
"""
Check if the predicted location matches any of the most common locations.
Args:
location_query (dict): Predicted location details.
most_common_locations (List[str]): List of most common locations.
Returns:
bool: True if a match is found, False otherwise.
"""
name = location_query['name']
admin1 = location_query['admin1']
cc = location_query['cc']
for loc in most_common_locations:
if name in loc and admin1 in loc and cc in loc:
return True
return False
def get_city_geojson(location_name: str) -> Optional[dict]:
"""
Fetch the GeoJSON data for the specified city.
Args:
location_name (str): Name of the city.
Returns:
Optional[dict]: GeoJSON data of the city or None if fetching fails.
"""
geolocator = Nominatim(user_agent="predictGeolocforImage")
try:
location = geolocator.geocode(location_name, geometry='geojson')
return location.raw['geojson'] if location else None
except Exception as e:
st.error(f"Failed to geocode location: {e}")
return None
def get_media(url: str) -> Optional[List[Tuple[str, str]]]:
"""
Fetch media URLs and associated text from the specified URL.
Args:
url (str): URL to fetch media from.
Returns:
Optional[List[Tuple[str, str]]]: List of tuples containing media URLs and associated text or None if fetching fails.
"""
try:
response = requests.get(url)
response.raise_for_status()
data = response.json()
return [(media['media_url'], entry['full_text'])
for entry in data for media in entry.get('media', []) if 'media_url' in media]
except requests.RequestException as e:
st.error(f"Failed to fetch media URL: {e}")
return None
def predict_location(image: Image, model: Geolocalizer) -> Optional[Tuple[List[float], dict, Optional[dict], float]]:
"""
Predict the location from the input image using the specified model.
Args:
image (Image): Input image.
model (Geolocalizer): Geolocation model.
Returns:
Optional[Tuple[List[float], dict, Optional[dict], float]]: Predicted GPS coordinates, location query, city GeoJSON data, and processing time or None if prediction fails.
"""
with st.spinner('Processing image and predicting location...'):
start_time = time.time()
try:
img_tensor = transform_image(image)
gps_radians = model(img_tensor)
gps_degrees = torch.rad2deg(gps_radians).squeeze(0).cpu().tolist()
location_query = rg.search((gps_degrees[0], gps_degrees[1]))[0]
location_name = f"{location_query['name']}, {location_query['admin1']}, {location_query['cc']}"
city_geojson = get_city_geojson(location_name)
processing_time = time.time() - start_time
return gps_degrees, location_query, city_geojson, processing_time
except Exception as e:
st.error(f"Failed to predict the location: {e}")
return None
def display_map(city_geojson: dict, gps_degrees: List[float]) -> None:
"""
Display a map with the specified city GeoJSON data and GPS coordinates.
Args:
city_geojson (dict): GeoJSON data of the city.
gps_degrees (List[float]): GPS coordinates.
"""
map_view = pdk.Deck(
map_style='mapbox://styles/mapbox/light-v9',
initial_view_state=pdk.ViewState(
latitude=gps_degrees[0],
longitude=gps_degrees[1],
zoom=8,
pitch=0,
),
layers=[
pdk.Layer(
'GeoJsonLayer',
data=city_geojson,
get_fill_color=[255, 180, 0, 140],
pickable=True,
stroked=True,
filled=True,
extruded=False,
line_width_min_pixels=1,
),
],
)
st.pydeck_chart(map_view)
def display_image(image_url: str) -> None:
"""
Display an image from the specified URL.
Args:
image_url (str): URL of the image.
"""
try:
response = requests.get(image_url)
response.raise_for_status()
image_bytes = BytesIO(response.content)
st.image(image_bytes, caption=f'Image from URL: {image_url}', use_column_width=True)
except requests.RequestException as e:
st.error(f"Failed to fetch image at URL {image_url}: {e}")
except Exception as e:
st.error(f"An error occurred: {e}")
def scrape_webpage(url: str) -> Union[Tuple[Optional[str], Optional[List[str]]], Tuple[None, None]]:
"""
Scrape the specified webpage for text and images.
Args:
url (str): URL of the webpage to scrape.
Returns:
Union[Tuple[Optional[str], Optional[List[str]]], Tuple[None, None]]: Extracted text and list of image URLs or None if scraping fails.
"""
with st.spinner('Scraping web page...'):
try:
response = requests.get(url)
response.raise_for_status()
soup = BeautifulSoup(response.content, 'html.parser')
base_url = url # Adjust based on tags or other HTML clues
text = ''.join(p.text for p in soup.find_all('p'))
images = [urljoin(base_url, img['src']) for img in soup.find_all('img') if 'src' in img.attrs]
return text, images
except requests.RequestException as e:
st.error(f"Failed to fetch and parse the URL: {e}")
return None, None
def main() -> None:
"""
Main function to run the Streamlit app.
"""
st.title('Welcome to Geolocation Guesstimation Demo 👋')
page = st.sidebar.selectbox(
"Choose your action:",
("Home", "Images", "Social Media", "Web Pages"),
index=0
)
st.sidebar.success("Select a demo above.")
st.sidebar.info(
"""
- Web App URL:
"""
)
st.sidebar.title("Contact")
st.sidebar.info(
"""
Yunus Serhat Bıçakçı at [yunusserhat.com](https://yunusserhat.com) | [GitHub](https://github.com/yunusserhat) | [Twitter](https://twitter.com/yunusserhat) | [LinkedIn](https://www.linkedin.com/in/yunusserhat)
"""
)
if page == "Home":
st.write("Welcome to the Geolocation Predictor. Please select an action from the sidebar dropdown.")
elif page == "Images":
upload_images_page()
elif page == "Social Media":
social_media_page()
elif page == "Web Pages":
web_page_url_page()
def upload_images_page() -> None:
"""
Display the image upload page for geolocation prediction.
"""
st.header("Image Upload for Geolocation Prediction")
uploaded_files = st.file_uploader("Choose images...", type=["jpg", "jpeg", "png"], accept_multiple_files=True)
if uploaded_files:
for idx, file in enumerate(uploaded_files, start=1):
with st.spinner(f"Processing {file.name}..."):
image = Image.open(file).convert('RGB')
st.image(image, caption=f'Uploaded Image: {file.name}', use_column_width=True)
model = load_geoloc_model()
if model:
result = predict_location(image, model)
if result:
gps_degrees, location_query, city_geojson, processing_time = result
st.write(
f"City: {location_query['name']}, Region: {location_query['admin1']}, Country: {location_query['cc']}")
if city_geojson:
display_map(city_geojson, gps_degrees)
st.write(f"Processing Time (seconds): {processing_time}")
def social_media_page() -> None:
"""
Display the social media analysis page.
"""
st.header("Social Media Analyser")
social_media_url = st.text_input("Enter a social media URL to analyse:", key='social_media_url_input')
if social_media_url:
media_data = get_media(social_media_url)
if media_data:
full_text = media_data[0][1]
st.subheader("Full Text")
st.write(full_text)
most_used_location, most_common_locations = most_frequent_locations(full_text)
st.subheader("Most Frequent Location")
st.write(most_used_location)
for idx, (media_url, _) in enumerate(media_data, start=1):
st.subheader(f"Image {idx}")
response = requests.get(media_url)
if response.status_code == 200:
image = Image.open(BytesIO(response.content)).convert('RGB')
st.image(image, caption=f'Image from URL: {media_url}', use_column_width=True)
model = load_geoloc_model()
if model:
result = predict_location(image, model)
if result:
gps_degrees, location_query, city_geojson, processing_time = result
location_name = f"{location_query['name']}, {location_query['admin1']}, {location_query['cc']}"
st.write(
f"City: {location_query['name']}, Region: {location_query['admin1']}, Country: {location_query['cc']}")
if city_geojson:
display_map(city_geojson, gps_degrees)
st.write(f"Processing Time (seconds): {processing_time}")
if check_location_match(location_query, most_common_locations):
st.success(
f"The predicted location {location_name} matches one of the most frequently mentioned locations!")
else:
st.error(f"Failed to fetch image at URL {media_url}: HTTP {response.status_code}")
def web_page_url_page() -> None:
"""
Display the web page URL analysis page.
"""
st.header("Web Page Analyser")
web_page_url = st.text_input("Enter a web page URL to scrape:", key='web_page_url_input')
if web_page_url:
text, images = scrape_webpage(web_page_url)
if text:
st.subheader("Extracted Text First 500 Characters:")
st.write(text[:500])
most_used_location, most_common_locations = most_frequent_locations(text)
st.subheader("Most Frequent Location")
st.write(most_used_location)
if images:
selected_image_url = st.selectbox("Select an image to predict location:", images)
if selected_image_url:
response = requests.get(selected_image_url)
if response.status_code == 200:
image = Image.open(BytesIO(response.content)).convert('RGB')
st.image(image, caption=f'Selected Image from URL: {selected_image_url}', use_column_width=True)
model = load_geoloc_model()
if model:
result = predict_location(image, model)
if result:
gps_degrees, location_query, city_geojson, processing_time = result
location_name = f"{location_query['name']}, {location_query['admin1']}, {location_query['cc']}"
st.write(
f"City: {location_query['name']}, Region: {location_query['admin1']}, Country: {location_query['cc']}")
if city_geojson:
display_map(city_geojson, gps_degrees)
st.write(f"Processing Time (seconds): {processing_time}")
if check_location_match(location_query, most_common_locations):
st.success(
f"The predicted location {location_name} matches one of the most frequently mentioned locations!")
if __name__ == '__main__':
main()