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c75ee0b 1367d73 c75ee0b 1367d73 c75ee0b 1367d73 c75ee0b 1367d73 c75ee0b 1367d73 c75ee0b 1367d73 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | import streamlit as st
import folium
from streamlit_folium import st_folium
from collections import defaultdict
# from joblib import Parallel, delayed
from post_assesment import get_sentiment, emotions
from post_search import search_posts_parallel
emotion_to_color = {
'no_emotion': "#666666",
'joy': "#33cc33",
'sadness': "#0066ff",
'surprise': "#ff9900",
'fear': "#aa2fd6",
'anger': "#ff0000"
}
POSTS_CNT = 500
# TOP_CITIES_CNT = 2
NUM_OF_WORKERS = 8
# top_cities = cities_db.nlargest(n=TOP_CITIES_CNT, columns="population")
# === Beginning of the page ===
st.title("Sentiment analysis")
topic = st.text_input("Enter your topic:", "котики")
button = st.button("Start!")
if button:
st.session_state["running"] = True
st.session_state.pop("results", None)
st.session_state.pop("posts", None)
if st.session_state.get("running", False):
st.text("Processing query...")
st.session_state["posts"] = search_posts_parallel(topic, POSTS_CNT)
# posts_per_city = Parallel(n_jobs=NUM_OF_WORKERS) \
# (
# delayed(search_posts_by_pos)(topic, POSTS_CNT, city_row["city"], city_row["lat"], city_row["lon"])
# for ind, city_row in top_cities.iterrows()
# )
# posts = [post for city_list in posts_per_city for post in city_list]
# print(*[post.owner_id for post in posts], sep='\n', flush=True)
# st.session_state["posts"] = posts
st.text("Gathered posts...")
st.session_state["results"] = get_sentiment(st.session_state["posts"])
# st.write(st.session_state["results"])
st.session_state["running"] = False
if "results" in st.session_state:
print("Got results!", flush=True)
posts = st.session_state["posts"]
results = st.session_state["results"]
scores = defaultdict(lambda: {e: 0.0 for e in emotions})
cnt = defaultdict(int)
names = {}
for i in range(len(posts)):
pos = posts[i].geolocation
names[pos] = posts[i].city_of_origin
cnt[pos] += 1
# for label, score in results[i].items():
# scores[pos][label] = score
scores[pos][results[i]["label"]] = results[i]["score"] if results[i]["label"] != "no_emotion" else 0.001
colors = {pos: emotion_to_color[max(score, key=score.get)] for pos, score in scores.items()}
map_table = {
"lon": [pos[0] for pos in cnt.keys()],
"lat": [pos[1] for pos in cnt.keys()],
"color": colors,
"size": cnt.values()
}
m = folium.Map()
for pos in cnt.keys():
# print(pos)
folium.CircleMarker((float(pos[0]), float(pos[1])), radius=cnt[pos] / POSTS_CNT * 100, color=colors[pos]).add_to(m)
st_folium(m, width=725, returned_objects=[])
# st.map(map_table, latitude="lat", longitude="lon", color="color", size="size") |