add gradio server and emotion detection

README.md

@@ -1,6 +1,6 @@
 ---
 title: PsyPlus
-emoji: 🌖
+emoji: 🤖
 colorFrom: green
 colorTo: purple
 sdk: gradio

app.py

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+import re, time
+import matplotlib.pyplot as plt
+from threading import Timer
+import gradio as gr
+
+from transformers import (
+  GPT2LMHeadModel, GPT2Tokenizer,
+  AutoModelForSequenceClassification, AutoTokenizer,
+  pipeline
+)
+# reference: https://huggingface.co/spaces/bentrevett/emotion-prediction
+# and https://huggingface.co/spaces/tareknaous/Empathetic-DialoGPT
+
+def euc_100():
+  # 1,2,3. asks about the user's emotions and store data
+  print('How was your day?')
+  print('On the scale 1 to 10, how would you judge your emotion through the following categories:') # ~ Baymax :)
+  emotion_types = ['overall'] #, 'happiness', 'surprise', 'sadness', 'depression', 'anger', 'fear', 'anxiety']
+  emotion_degree = []
+  input_time = []
+
+  for e in emotion_types:
+    while True:
+      x = input(f'{e}: ')
+      if x.isnumeric() and (0 < int(x) < 11):
+        emotion_degree.append(int(x))
+        input_time.append(time.gmtime())
+        break
+      else:
+        print('invalid input, my friend :) plz input again')
+
+  # 4. if good mood
+  if emotion_degree[0] >= 6:
+    print('You seem to be in a good mood today. Is there anything you could notice that makes you happy?')
+    while True:
+      # timer = Timer(10, ValueError)
+      # timer.start()
+      x = input('Your answer: ')
+      if x == '': # need to change this part to waiting 10 seconds
+        print('Whether your good mood is over?')
+        print('Any other details that you would like to recall?')
+        y = input('Your answer (Yes or No): ')
+        if y == 'No':
+          break
+      else:
+        break
+    print('I am glad that you are willing to share the experience with me. Thanks for letting me know.')
+  
+  # 5. bad mood
+  else:
+    questions = [
+      'What specific thing is bothering you the most right now?',
+      'Oh, I see. So when it is happening, what feelings or emotions have you got?',
+      'And what do you think about those feelings or emotions at that time?',
+      'Could you think of any evidence for your above-mentioned thought?',
+    ]
+    for q in questions:
+      print(q)
+      y = 'No' # bad mood
+      while True:
+        x = input('Your answer (example of answer here): ')
+        if x == '': # need to change this part to waiting 10 seconds
+          print('Whether your bad mood is over?')
+          y = input('Your answer (Yes or No): ')
+          if y == 'Yes':
+            break
+        else:
+          break
+      if y == 'Yes':
+        print('Nice to hear that.')
+        break
+
+    # reading interface here
+    print('Here are some reference articles about bad emotions. You can take a look :)')
+    pass
+
+
+def load_neural_emotion_detector():
+  model_name = "joeddav/distilbert-base-uncased-go-emotions-student"
+  model = AutoModelForSequenceClassification.from_pretrained(model_name)
+  tokenizer = AutoTokenizer.from_pretrained(model_name)
+  pipe = pipeline('text-classification', model=model, tokenizer=tokenizer, 
+                  return_all_scores=True, truncation=True)
+  return pipe
+
+def sort_predictions(predictions):
+  return sorted(predictions, key=lambda x: x['score'], reverse=True)
+
+def plot_emotion_distribution(predictions):
+  fig, ax = plt.subplots()
+  ax.bar(x=[i for i, _ in enumerate(prediction)],
+         height=[p['score'] for p in prediction],
+         tick_label=[p['label'] for p in prediction])
+  ax.tick_params(rotation=90)
+  ax.set_ylim(0, 1)
+  plt.show()
+
+def rulebase(text):
+  keywords = {
+    'life_safety': ["death", "suicide", "murder", "to perish together", "jump off the building"],
+    'immediacy': ["now", "immediately", "tomorrow", "today"],
+    'manifestation': ["never stop", "every moment", "strong", "very"]
+  }
+
+  # if found dangerous kw/topics
+  if re.search(rf"{'|'.join(keywords['life_safety'])}", text)!=None and \
+      sum([re.search(rf"{'|'.join(keywords[k])}", text)!=None for k in ['immediacy','manifestation']]) >= 1:
+    print('We noticed that you may need immediate professional assistance, would you like to make a phone call? '
+      'The Hong Kong Lifeline number is (852) 2382 0000')
+    x = input('Choose 1. "Dial to the number" or 2. "No dangerous emotion la": ')
+    if x == '1':
+      print('Let you connect to the office')
+    else:
+      print('Sorry for our misdetection. We just want to make sure that you could get immediate help when needed. '
+        'Would you mind if we send this conversation to the cloud to finetune the model.')
+      y = input('Yes or No: ')
+      if y == 'Yes':
+        pass # do smt here
+
+
+def euc_200(text, testing=True):
+  # 2. using rule to judge user's emotion
+  rulebase(text)
+
+  # 3. using ML
+  if not testing:
+    pipe = load_neural_emotion_detector()
+    prediction = pipe(text)[0]
+    prediction = sort_predictions(prediction)
+    plot_emotion_distribution(prediction)
+  
+  # get the most probable emotion. TODO: modify this part, may take sum of prob. over all negative emotion
+  threshold = 0.3
+  emotion = {'label': 'sadness', 'score': 0.4} if testing else prediction[0]
+  # then judge
+  if emotion['label'] in ['surprise', 'sadness', 'anger', 'fear'] and emotion['score'] > threshold:
+    print(f'It has come to our attention that you may suffer from {emotion["label"]}')
+    print('If you want to know more about yourself, '
+      'some professional scales are provided to quantify your current status. '
+      'After a period of time (maybe a week/two months/a month) trying to follow the solutions we suggested, '
+      'you can fill out these scales again to see if you have improved.')
+    x = input('Fill in the form now (Okay or Later): ')
+    if x == 'Okay':
+      print('Display the form')
+    else:
+      print('Here are some reference articles about bad emotions. You can take a look :)')
+
+  # 4. If both of the above are not satisfied. What do u mean by 'satisfied' here?
+  questions = [
+    'What specific thing is bothering you the most right now?',
+    'Oh, I see. So when it is happening, what feelings or emotions have you got?',
+    'And what do you think about those feelings or emotions at that time?',
+    'Could you think of any evidence for your above-mentioned thought? #',
+  ]
+  for q in questions:
+    print(q)
+    y = 'No' # bad mood
+    while True:
+      x = input('Your answer (example of answer here): ')
+      if x == '': # need to change this part to waiting 10 seconds
+        print('Whether your bad mood is over?')
+        y = input('Your answer (Yes or No): ')
+        if y == 'Yes':
+          break
+      else:
+        break
+    if y == 'Yes':
+      print('Nice to hear that.')
+      break
+
+  # reading interface here
+  print('Here are some reference articles about bad emotions. You can take a look :)')
+  pass
+
+
+tokenizer = GPT2Tokenizer.from_pretrained("tareknaous/dialogpt-empathetic-dialogues")
+model = GPT2LMHeadModel.from_pretrained("tareknaous/dialogpt-empathetic-dialogues")
+model.eval()
+
+def chat(message, history):
+  history = history or []
+  eos = tokenizer.eos_token
+  input_str = eos.join([m, r for m, r in history])
+
+  bot_input_ids = tokenizer.encode(input_str, return_tensors='pt')
+  bot_output_ids = model.generate(bot_input_ids,
+                                  max_length=1000, 
+                                  pad_token_id=tokenizer.eos_token_id)
+  message = tokenizer.decode(bot_output_ids[:, bot_input_ids.shape[-1]:][0], 
+                             skip_special_tokens=True)
+
+  history.append((message, response))
+  return history, history
+
+
+if __name__ == '__main__':
+  # euc_100()
+  # euc_200('I am happy about my academic record.')
+  title = "PsyPlus Empathetic Chatbot"
+  description = "Gradio demo for product of PsyPlus. Based on rule-based CBT and conversational AI model DialoGPT"
+  iface = gr.Interface(
+    chat,
+    ["text", "state"],
+    ["chatbot", "state"],
+    allow_screenshot=False,
+    allow_flagging="never",
+    title=title,
+    description=description
+  )
+  iface.launch(debug=True)

requirement.txt

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+torch
+transformers
+matplotlib