Delete app.py

app.py

@@ -1,140 +0,0 @@
-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
-
-consumer_key = "sHz78Xj5Dl41cqfzEHVoRcaKo"
-consumer_secret = "3y5caZfu91nmB2MNH7mDSu5Cgf5qaVRpMfbDoCPW4dU7E46k03"
-access_key = "1116912581434695680-x359MscPSdqEcJzoIlg4jMsCZRdyNX"
-access_secret = "wEsALFUava2TnYXWnuacrzSK4eiYfJUFLBRWPqGuMRnTz"
-
-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 = dict()
-    response["labels"] = [pred["label"] for pred in preds]
-    response["scores"] = [pred["score"] for pred in preds]
-    return response
-
-
-def neutralise_sentiment(preds):
-    for i, (label, score) in enumerate(zip(preds["labels"], preds["scores"])):
-        if score < 0.5:
-            preds["labels"][i] = "neutral"
-            preds["scores"][i] = 1.0 - score
-
-
-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
-
-
-"""
-# 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"])
-    # neutralise_sentiment(preds)
-    tweets.update(preds)
-    # dataframe creation + preprocessing
-    df = pd.DataFrame(tweets)
-    df["timestamps"] = pd.to_datetime(df["timestamps"])
-    # plots
-    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)
-
-    # TODO: check that stacking makes sense!
-    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)
-
-    # tweet sample
-    st.markdown(df.to_markdown())