Create app.py

app.py

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+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())