requirements and app file

app.py

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+import gradio as gr
+import requests
+import os
+import json
+from transformers import pipeline
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+
+
+
+bearer_token = os.environ.get("BEARER_TOKEN")
+print(bearer_token)
+
+
+search_url = "https://api.twitter.com/2/tweets/search/recent"
+
+
+
+def bearer_oauth(r):
+    """
+    Method required by bearer token authentication.
+    """
+
+    r.headers["Authorization"] = f"Bearer {bearer_token}"
+    r.headers["User-Agent"] = "v2RecentSearchPython"
+    return r
+
+def connect_to_endpoint(url, params):
+    response = requests.get(url, auth=bearer_oauth, params=params)
+    print(response.status_code)
+    if response.status_code != 200:
+        raise Exception(response.status_code, response.text)
+    return response.json()
+
+
+def fetch_tweets(tag):
+    q = "\"" + tag + "\""
+    query_params = {'query': q, 'tweet.fields': 'author_id', 'max_results': 100}
+    json_response = connect_to_endpoint(search_url, query_params)
+    #print(json.dumps(json_response, indent=4, sort_keys=True))
+    phrases = []
+    for entry in json_response["data"]:
+      phrases.append(entry["text"])
+    return phrases
+
+pipe = pipeline("text-classification", model="mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis")
+
+def analyze_phrases(phrases):
+  positive = 0
+  positive_examples = {}
+  negative = 0
+  negative_examples = {}
+  neutral = 0
+  neutral_examples = {}
+  outputs = pipe(phrases)
+  for index, x in enumerate(outputs):
+    if x['label'] == 'positive':
+      positive += 1
+      if positive <= 3:
+        positive_examples[phrases[index]] =  x['score']
+    elif x['label'] == 'neutral':
+      neutral += 1
+      if neutral <= 3:
+        neutral_examples[phrases[index]] =  x['score']
+    elif x['label'] == 'negative':
+      negative += 1
+      if negative <= 3:
+        negative_examples[phrases[index]] =  x['score']
+    else:
+      pass
+  counts = [positive, neutral, negative]
+  return counts, positive_examples, neutral_examples, negative_examples
+
+
+def calculate_sentiment(tag):
+  phrases = fetch_tweets(tag)
+  counts, positive_examples, neutral_examples, negative_examples = analyze_phrases(phrases)
+  output = "positive: " + str(counts[0]) + "\n" + "neutral: " + str(counts[1]) + "\n" + "negative: " + str(counts[2])
+  plt.style.use('_mpl-gallery-nogrid')
+
+
+  # make data
+  colors = ['green', 'yellow', 'red']
+  labels = ["Positive", "Neutral", "Negative"]
+
+  # plot
+  fig, ax = plt.subplots(figsize=(10, 6))
+  wedges, texts = ax.pie(counts, colors=colors, radius=3, center=(4, 4),
+        wedgeprops={"linewidth": 1, "edgecolor": "white"}, labeldistance=1.05)
+
+  # Create a legend
+  ax.legend(wedges, labels, title="Categories", loc="center left", bbox_to_anchor=(1, 0, 0.5, 1))
+
+  ax.set(xlim=(0, 8),
+        ylim=(0, 8))
+  print(positive_examples)
+  html_content = ""
+  positive_tweets = list(positive_examples.items())
+  p_df = pd.DataFrame(positive_tweets, columns=["Tweet", "Confidence"])
+  positive_table = p_df.to_html(index=False)
+
+  neutral_tweets = list(neutral_examples.items())
+  n_df = pd.DataFrame(neutral_tweets, columns=["Tweet", "Confidence"])
+  neutral_table = n_df.to_html(index=False)
+
+  negative_tweets = list(negative_examples.items())
+  neg_df = pd.DataFrame(negative_tweets, columns=["Tweet", "Confidence"])
+  negative_table = neg_df.to_html(index=False)
+
+  html_content += f"<h2>Recent Positive Tweets</h2>" + positive_table
+  html_content += f"<h2>Recent Negative Tweets</h2>" + negative_table
+  
+  
+  return fig, html_content
+    
+
+iface = gr.Interface(fn=calculate_sentiment, inputs="text", outputs=["plot","html"])
+iface.launch(debug=True)

requirements.txt

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+requests
+os
+json
+transformers
+matplotlib
+numpy
+pandas