import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import streamlit as st
import tweepy
from plotly.subplots import make_subplots
from transformers import pipeline

# Twitter API keys (should be stored securely, e.g., in environment variables)
consumer_key = "your_consumer_key"
consumer_secret = "your_consumer_secret"
access_key = "your_access_key"
access_secret = "your_access_secret"

auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_key, access_secret)
api = tweepy.API(auth)

def get_tweets(username, count):
    tweets = tweepy.Cursor(api.user_timeline, screen_name=username, tweet_mode="extended", exclude_replies=True, include_rts=False).items(count)
    tweets = list(tweets)
    response = {
        "tweets": [tweet.full_text.replace("\n", "").lower() for tweet in tweets],
        "timestamps": [str(tweet.created_at) for tweet in tweets],
        "retweets": [tweet.retweet_count for tweet in tweets],
        "likes": [tweet.favorite_count for tweet in tweets],
    }
    return response

def get_sentiment(texts):
    preds = pipe(texts)
    response = {
        "labels": [pred["label"] for pred in preds],
        "scores": [pred["score"] for pred in preds],
    }
    return response

def get_aggregation_period(df):
    t_min, t_max = df["timestamps"].min(), df["timestamps"].max()
    t_delta = t_max - t_min
    if t_delta < pd.to_timedelta("30D"):
        return "1D"
    elif t_delta < pd.to_timedelta("365D"):
        return "7D"
    else:
        return "30D"

@st.cache_data
def load_model():
    pipe = pipeline(task="sentiment-analysis", model="bhadresh-savani/distilbert-base-uncased-emotion")
    return pipe

# Streamlit app
st.title("Twitter Emotion Analyser")

pipe = load_model()
twitter_handle = st.sidebar.text_input("Twitter handle:", "elonmusk")
twitter_count = st.sidebar.selectbox("Number of tweets:", (10, 30, 50, 100))

if st.sidebar.button("Get tweets!"):
    tweets = get_tweets(twitter_handle, twitter_count)
    preds = get_sentiment(tweets["tweets"])
    tweets.update(preds)
    
    df = pd.DataFrame(tweets)
    df["timestamps"] = pd.to_datetime(df["timestamps"])
    
    agg_period = get_aggregation_period(df)
    ts_sentiment = df.groupby(["timestamps", "labels"]).count()["likes"].unstack().resample(agg_period).count().stack().reset_index()
    ts_sentiment.columns = ["timestamp", "label", "count"]

    fig = make_subplots(rows=1, cols=2, horizontal_spacing=0.15)

    for label in ts_sentiment["label"].unique():
        fig.add_trace(go.Scatter(x=ts_sentiment.query("label == @label")["timestamp"], y=ts_sentiment.query("label == @label")["count"], mode="lines", name=label, stackgroup="one", hoverinfo="x+y"), row=1, col=1)

    likes_per_label = df.groupby("labels")["likes"].mean().reset_index()

    fig.add_trace(go.Bar(x=likes_per_label["labels"], y=likes_per_label["likes"], showlegend=False, marker_color=px.colors.qualitative.Plotly, opacity=0.6), row=1, col=2)

    fig.update_yaxes(title_text="Number of Tweets", row=1, col=1)
    fig.update_yaxes(title_text="Number of Likes", row=1, col=2)
    fig.update_layout(height=350, width=750)

    st.plotly_chart(fig)
    st.markdown(df.to_markdown())