Added a playground

module_grading_playground.py

@@ -0,0 +1,94 @@
+import json
+
+import openai
+import os
+from dotenv import load_dotenv
+from functools import reduce
+
+load_dotenv()
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+chat_config = {'max_tokens': 1000, 'temperature': 0.2}
+section_keys = ['reading', 'listening', 'writing', 'speaking', 'level']
+grade_top_limit = 9
+
+tools = [{
+    "type": "function",
+    "function": {
+        "name": "save_evaluation_and_suggestions",
+        "description": "Saves the evaluation and suggestions requested by input.",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "evaluation": {
+                    "type": "string",
+                    "description": "A comment on the IELTS section grade obtained in the specific section and what it could mean without suggestions.",
+                },
+                "suggestions": {
+                    "type": "string",
+                    "description": "A small paragraph text with suggestions on how to possibly get a better grade than the one obtained.",
+                },
+            },
+            "required": ["evaluation", "suggestions"],
+        },
+    }
+}]
+
+
+# Input Format
+# {'sections': Array of {'code': key, 'name': name, 'grade': grade}}
+# Output
+# {'sections': Array of {'code': key, 'name': name, 'grade': grade, 'evaluation': evaluation, 'suggestions': suggestions}}
+
+def grading_summary(body):
+    extracted_sections = extract_existing_sections_from_body(body, section_keys)
+
+    ret = []
+
+    for section in extracted_sections:
+        openai_response_dict = calculate_section_grade_summary(section)
+        ret = ret + [{'code': section['code'], 'name': section['name'], 'grade': section['grade'],
+                      'evaluation': openai_response_dict['evaluation'],
+                      'suggestions': openai_response_dict['suggestions']}]
+
+    return {'sections': ret}
+
+
+def calculate_section_grade_summary(section):
+    res = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo",
+        max_tokens=chat_config['max_tokens'],
+        temperature=chat_config['temperature'],
+        tools=tools,
+        messages=[
+            {
+                "role": "user",
+                "content": "You are a IELTS test section grade evaluator. You will receive a IELTS test section name and the grade obtained in the section. You should offer a comment on this grade with also suggestions on how to possibly get a better grade.",
+            },
+            {
+                "role": "user",
+                "content": "Section: " + str(section['name']) + " Grade: " + str(section['grade']),
+            },
+            {"role": "user", "content": "Speak in third person."},
+            {"role": "user", "content": "Please save the evaluation and suggestions generated."}
+        ])
+
+    return parse_openai_response(res)
+
+
+def parse_openai_response(response):
+    if 'choices' in response and len(response['choices']) > 0 and 'message' in response['choices'][
+        0] and 'tool_calls' in response['choices'][0]['message'] and isinstance(
+        response['choices'][0]['message']['tool_calls'], list) and len(
+        response['choices'][0]['message']['tool_calls']) > 0 and \
+            response['choices'][0]['message']['tool_calls'][0]['function']['arguments']:
+        return json.loads(response['choices'][0]['message']['tool_calls'][0]['function']['arguments'])
+    else:
+        return {'evaluation': "", 'suggestions': ""}
+
+
+def extract_existing_sections_from_body(my_dict, keys_to_extract):
+    if 'sections' in my_dict and isinstance(my_dict['sections'], list) and len(my_dict['sections']) > 0:
+        return list(filter(
+            lambda item: 'code' in item and item['code'] in keys_to_extract and 'grade' in item and 'name' in item,
+            my_dict['sections']))