Update app.py

app.py

@@ -13,8 +13,32 @@ classifier_emotions = ['positive', 'neutral', 'negative']
 
 classifier = pipeline('text-classification', model=classifier_model_name)
 
-def my_inference_function(name):
-    return "Hello " + name + "!"
+def find_emotional_sentences(text, emotions, threshold):
+    sentences_by_emotion = {}
+    for e in emotions:
+        sentences_by_emotion[e]=[]
+    sentences = nltk.sent_tokenize(text)
+    print(f'Document has {len(text)} characters and {len(sentences)} sentences.')
+    for s in sentences:
+        prediction = classifier(s)
+        if (prediction[0]['label']!='neutral' and prediction[0]['score']>threshold):
+            #print (f'Sentence #{sentences.index(s)}: {prediction} {s}')
+            sentences_by_emotion[prediction[0]['label']].append(s)
+    for e in emotions:
+        print(f'{e}: {len(sentences_by_emotion[e])} sentences')
+    return sentences_by_emotion
+
+def summarize_sentences(sentences_by_emotion, min_length, max_length):
+    for k in sentences_by_emotion.keys():
+        if (len(sentences_by_emotion[k])!=0):
+            text = ' '.join(sentences_by_emotion[k])
+            summary = summarizer(text, min_length=min_length, max_length=max_length)
+            print(f"{k.upper()}: {summary[0]['summary_text']}\n")
+
+
+def my_inference_function(text):
+    sentences_by_emotion = find_emotional_sentences(text, classifier_emotions, 0.7)
+    return sentences_by_emotion
 
 gr_interface = gradio.Interface(
     fn = my_inference_function,