Fix CI lint

Author: echuraev

.github/workflows/lint.yml

@@ -27,7 +27,7 @@ jobs:
         run: echo "PYTHONPATH=$(pwd)" >> $GITHUB_ENV
 
       - name: Run Black
-        run: black --check .
+        run: black --check --diff .
 
       - name: Run isort
         run: isort --check-only --diff .

training_and_examples/mobile_apps/emotional_dialogue/web_server/server.py

@@ -1,134 +1,130 @@
-import os
 import io
-import sys
-from subprocess import call
-from flask_cors import CORS
-from flask import Flask, jsonify, send_file, Response
-from flask import request
 import json
+import os
+import sys
 import uuid
-import requests
+from subprocess import call
 
+import requests
 import urllib3
+from flask import Flask, Response, jsonify, request, send_file
+from flask_cors import CORS
+
 urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
 
 app = Flask(__name__)
 CORS(app)
 
-@app.route('/')
-def status_check():
-    return 'Server is running'
 
+@app.route("/")
+def status_check():
+    return "Server is running"
 
 
 if True:
+    import os
+
     import httpx
     from openai import OpenAI
-    import os
-      
-    os.environ["OPENAI_API_KEY"] = "" #use your openai credentials or repace this block tu your LLM
+
+    os.environ[
+        "OPENAI_API_KEY"
+    ] = ""  # use your openai credentials or repace this block tu your LLM
     os.environ["OPENAI_PROXY_URL"] = ""
-     
+
     proxy_url = os.environ.get("OPENAI_PROXY_URL")
-    client = OpenAI() if proxy_url is None or proxy_url == "" else OpenAI(http_client=httpx.Client(proxy=proxy_url))       
+    client = (
+        OpenAI()
+        if proxy_url is None or proxy_url == ""
+        else OpenAI(http_client=httpx.Client(proxy=proxy_url))
+    )
     print(client)
 
-    model_name="gpt-4o" 
+    model_name = "gpt-4o"
+
     def get_completion(content):
         response = client.chat.completions.create(
-            model=model_name,
-            messages=[{"role": "user", "content": content}],
-            temperature=0
+            model=model_name, messages=[{"role": "user", "content": content}], temperature=0
         )
         return response.choices[0].message.content
 
     def get_completion_from_messages(messages):
         response = client.chat.completions.create(
-            model=model_name,
-            messages=messages,
-            temperature=0
+            model=model_name, messages=messages, temperature=0
         )
         return response.choices[0].message.content
-    
-        
-emotions=["happy","sad","fear","angry"]
-emotion_prompt="What is the {} one-paragraph response to "
-additional_emotion_prompt="What is the {} one-paragraph response to the initial question?"
-aggregation_prompt="Aggregate the answers to form a final response "
-user_emotion_prompt=". Take into account that I am {} now."
 
-def get_emotion_prompt(emotion,prompt):
-    return emotion_prompt.format(emotion)+prompt
+
+emotions = ["happy", "sad", "fear", "angry"]
+emotion_prompt = "What is the {} one-paragraph response to "
+additional_emotion_prompt = "What is the {} one-paragraph response to the initial question?"
+aggregation_prompt = "Aggregate the answers to form a final response "
+user_emotion_prompt = ". Take into account that I am {} now."
+
+
+def get_emotion_prompt(emotion, prompt):
+    return emotion_prompt.format(emotion) + prompt
+
 
 def get_additional_emotion_prompt(emotion):
     return additional_emotion_prompt.format(emotion)
 
+
 def get_aggregation_prompt(user_emotion):
-    return aggregation_prompt if user_emotion=='neutral' else aggregation_prompt+user_emotion_prompt.format(user_emotion)
-
-def process_multiple_emotional_agents2(question,user_emotion='neutral'):
-    ai_answers={}
-    messages=[]
-    for i,emotion in enumerate(emotions):
-        if i==0:
-            messages.append({
-              "role": "user",
-              "content": get_emotion_prompt(emotion,question)
-            })
+    return (
+        aggregation_prompt
+        if user_emotion == "neutral"
+        else aggregation_prompt + user_emotion_prompt.format(user_emotion)
+    )
+
+
+def process_multiple_emotional_agents2(question, user_emotion="neutral"):
+    ai_answers = {}
+    messages = []
+    for i, emotion in enumerate(emotions):
+        if i == 0:
+            messages.append({"role": "user", "content": get_emotion_prompt(emotion, question)})
         else:
-            messages.append({
-              "role": "user",
-              "content": get_additional_emotion_prompt(emotion)
-            })
-        assistant=get_completion_from_messages(messages)
-        messages.append({
-          "role": "assistant",
-          "content": assistant
-        })
-        print(emotion,assistant,"\n\n")
-        ai_answers[emotion]=assistant
-        #time.sleep(delay)
-        
-    messages.append({
-      "role": "user",
-      "content": get_aggregation_prompt(user_emotion)
-    })
-    assistant=get_completion_from_messages(messages)
-    print('Summary:',assistant)
-    ai_answers['Summary']=assistant
+            messages.append({"role": "user", "content": get_additional_emotion_prompt(emotion)})
+        assistant = get_completion_from_messages(messages)
+        messages.append({"role": "assistant", "content": assistant})
+        print(emotion, assistant, "\n\n")
+        ai_answers[emotion] = assistant
+        # time.sleep(delay)
+
+    messages.append({"role": "user", "content": get_aggregation_prompt(user_emotion)})
+    assistant = get_completion_from_messages(messages)
+    print("Summary:", assistant)
+    ai_answers["Summary"] = assistant
     return ai_answers
-    #ai_answer="Summary (user emotion "+user_emotion+"): "+assistant
-    
-    
-@app.route('/insideout', methods=['POST'])
+    # ai_answer="Summary (user emotion "+user_emotion+"): "+assistant
+
+
+@app.route("/insideout", methods=["POST"])
 def process_insideout_request():
     content = request.json
     print(content)
-    #photo = request.files['photo'].read()
-    question=content['question']
-    user_emotion=content['userEmotion']
-    ai_answers=process_multiple_emotional_agents2(question,user_emotion)
-    return Response(response=json.dumps(ai_answers),
-                    status=200,
-                    mimetype="application/json")
-
-    
-@app.route('/single', methods=['POST'])
+    # photo = request.files['photo'].read()
+    question = content["question"]
+    user_emotion = content["userEmotion"]
+    ai_answers = process_multiple_emotional_agents2(question, user_emotion)
+    return Response(response=json.dumps(ai_answers), status=200, mimetype="application/json")
+
+
+@app.route("/single", methods=["POST"])
 def process_single_request():
     content = request.json
-    #print(content)
-    question=content['question']
-    user_emotion=content['userEmotion']
-    prompt=get_emotion_prompt(user_emotion,question)
-    #if user_emotion!='neutral':
+    # print(content)
+    question = content["question"]
+    user_emotion = content["userEmotion"]
+    prompt = get_emotion_prompt(user_emotion, question)
+    # if user_emotion!='neutral':
     #    prompt+=user_emotion_prompt.format(user_emotion)
-    assistant=get_completion(prompt)
-    print(prompt,'\n',assistant)
-    ai_answers={'Summary':assistant}
-    return Response(response=json.dumps(ai_answers),
-                    status=200,
-                    mimetype="application/json")
-
-                    
-if __name__ == '__main__':
+    assistant = get_completion(prompt)
+    print(prompt, "\n", assistant)
+    ai_answers = {"Summary": assistant}
+    return Response(response=json.dumps(ai_answers), status=200, mimetype="application/json")
+
+
+if __name__ == "__main__":
     app.run(host="0.0.0.0", port=5050)