Upload utils.py

Utility functions

utils.py

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+
+#utils.py
+import openai
+import os
+import easyocr
+import pandas as pd
+import plotly.graph_objects as go
+import plotly.express as px
+import matplotlib.pyplot as plt
+from nltk.sentiment.vader import SentimentIntensityAnalyzer
+import nltk
+nltk.download('vader_lexicon')
+
+def ocr_reader(lan, png):
+    locs = []; words = []; confidence = []
+    try:
+        reader = easyocr.Reader([str(lan)]) #Initialise Language
+        result = reader.readtext(str(png))
+        for i in result:
+            locs.append(i[0])
+            words.append(i[1])
+            confidence.append(i[2])
+        return locs, words, confidence
+    except Exception as e:
+        print(e)
+        
+def get_completion(prompt, model="gpt-3.5-turbo"):
+    messages = [{"role": "user", "content": prompt}]
+    response = openai.ChatCompletion.create(
+        model=model,
+        messages=messages,
+        temperature=0, # this is the degree of randomness of the model's output
+    )
+    content = response.choices[0].message["content"]
+    token_dict = {
+        'prompt_tokens':response['usage']['prompt_tokens'],
+        'completion_tokens':response['usage']['completion_tokens'],
+        'total_tokens':response['usage']['total_tokens'],
+            }
+    moderation_output = openai.Moderation.create(input=prompt)["results"][0]
+    return content, token_dict, moderation_output
+
+def get_radar(df):
+    df['names'] = df.index
+    fig = go.Figure()
+    fig.add_trace(go.Scatterpolar(
+                  r=df.category_scores,
+                  theta=df.names,
+                  fill='toself',
+                  name='Moderation'))
+    fig.update_layout(
+        polar=dict(radialaxis=dict(
+                      visible=True,
+                      range=[0, 1])),
+              showlegend=False)
+
+    return fig
+
+def plot_wordcloud(wc):
+    fig, ax = plt.subplots(figsize = (12, 8))
+    ax.imshow(wc, interpolation="bilinear")
+    plt.axis("off")
+    return fig
+
+def get_sentiment(text):
+    sentences = [' '.join(sent.split()).strip() for sent in text.split('.')]
+    df = pd.DataFrame(sentences, columns=['content'])
+    sid = SentimentIntensityAnalyzer()
+    df['sentiment'] = df['content'].apply(lambda x: sid.polarity_scores(x))  
+    df = pd.concat([df.drop(['sentiment'], axis=1), df['sentiment'].apply(pd.Series)], axis=1)
+    df = df.rename(columns={'neu': 'neutral', 'neg': 'negative', 'pos': 'positive'})
+    df['confidence'] = df[["negative", "neutral", "positive"]].max(axis=1)
+    df['sentiment'] = df[["negative", "neutral", "positive"]].idxmax(axis=1)
+    grouped = pd.DataFrame(df['sentiment'].value_counts()).reset_index()
+    grouped.columns = ['sentiment', 'count']
+    
+    fig = px.scatter(df, y='sentiment', color='sentiment', size='confidence', 
+                     hover_data=['content'], 
+                     color_discrete_map={"negative": "firebrick", "neutral": "navajowhite", "positive": "darkgreen"})
+    fig.update_layout(width=800,height=300,)
+    return df, fig
+
+