Ran black

test.py

@@ -12,31 +12,32 @@ from langchain.chains import LLMChain, SequentialChain
 llm = ChatOpenAI(temperature=0.0, openai_api_key=os.environ["OPENAI"])
 
 
-
-
 def generate_skills() -> list:
-
     template_generate_skills = """
     Can you generate me a list of skills you would need to be successfully employed in a Data Scientist role?
     Return 10 skills as a JSON list.
     """
 
-    prompt_generate_skills = ChatPromptTemplate.from_template(template=template_generate_skills)
-    role_skills = LLMChain(llm=llm, prompt=prompt_generate_skills, output_key="role_skills")
+    prompt_generate_skills = ChatPromptTemplate.from_template(
+        template=template_generate_skills
+    )
+    role_skills = LLMChain(
+        llm=llm, prompt=prompt_generate_skills, output_key="role_skills"
+    )
 
     generate_skills_chain = SequentialChain(
         chains=[role_skills],
         input_variables=[],
         output_variables=["role_skills"],
-        verbose=False
+        verbose=False,
     )
 
     result = generate_skills_chain({})
     result_array = json.loads(result["role_skills"])["skills"]
     return result_array
 
-def generate_resume(skills: list) -> str:
 
+def generate_resume(skills: list) -> str:
     template_generate_resume = """
     Given the following list of skills as an array delimited by three backticks, generate a resume of a data scientist with 3 years of experience.
     Make sure to include a section "skills" in the resume.
@@ -46,22 +47,24 @@ def generate_resume(skills: list) -> str:
     ```
     """
 
-    prompt_generate_resume = ChatPromptTemplate.from_template(template=template_generate_resume)
+    prompt_generate_resume = ChatPromptTemplate.from_template(
+        template=template_generate_resume
+    )
     resume = LLMChain(llm=llm, prompt=prompt_generate_resume, output_key="resume")
 
     generate_resume_chain = SequentialChain(
         chains=[resume],
         input_variables=["skills"],
         output_variables=["resume"],
-        verbose=False
+        verbose=False,
     )
 
     result = generate_resume_chain({"skills": skills})
 
     return result
 
+
 def retrieve_skills(resume: str) -> str:
-    
     template_retrieve_skills = """
     Given the following resume delimited by three backticks, retrieve the skills this data scientist possesses.
     Return them as a JSON list.
@@ -71,14 +74,16 @@ def retrieve_skills(resume: str) -> str:
     ```
     """
 
-    prompt_retrieve_skills = ChatPromptTemplate.from_template(template=template_retrieve_skills)
+    prompt_retrieve_skills = ChatPromptTemplate.from_template(
+        template=template_retrieve_skills
+    )
     skills = LLMChain(llm=llm, prompt=prompt_retrieve_skills, output_key="skills")
 
     retrieve_skills_chain = SequentialChain(
         chains=[skills],
         input_variables=["resume"],
         output_variables=["skills"],
-        verbose=False
+        verbose=False,
     )
 
     result = retrieve_skills_chain({"resume": resume})
@@ -86,10 +91,12 @@ def retrieve_skills(resume: str) -> str:
 
     return result_array
 
-def get_score(true_values:list, predicted_values:list) -> float:
+
+def get_score(true_values: list, predicted_values: list) -> float:
     intersection_list = [value for value in predicted_values if value in true_values]
     print(intersection_list)
-    return len(intersection_list)/len(true_values)
+    return len(intersection_list) / len(true_values)
+
 
 if __name__ == "__main__":
     role_skills = generate_skills()
@@ -107,16 +114,16 @@ if __name__ == "__main__":
 #         's3',
 #         region_name='eu-west-1'
 #     )
-    
-    # resumes = s3.get_object(Bucket='ausy-datalake-drift-nonprod', Key='resume-matcher/raw/resume-dataset.csv')
-
-    # resumes_list = resumes['Body'].read().decode('utf-8').splitlines()
-    # resumes_list = resumes['Body'].read().decode('utf-8').splitlines()
-    # resumes_list = str(resumes_list).replace('. ', '.\n')
-    # resumes_list = str(resumes_list).replace('•', '\n - ')
-    # resumes_list = [s.replace('. ', '.\n') for s in resumes_list]
-    # resumes_list = [s.replace('•', '\n - ') for s in resumes_list]
-    # resume_string =''.join(resumes_list)
+
+# resumes = s3.get_object(Bucket='ausy-datalake-drift-nonprod', Key='resume-matcher/raw/resume-dataset.csv')
+
+# resumes_list = resumes['Body'].read().decode('utf-8').splitlines()
+# resumes_list = resumes['Body'].read().decode('utf-8').splitlines()
+# resumes_list = str(resumes_list).replace('. ', '.\n')
+# resumes_list = str(resumes_list).replace('•', '\n - ')
+# resumes_list = [s.replace('. ', '.\n') for s in resumes_list]
+# resumes_list = [s.replace('•', '\n - ') for s in resumes_list]
+# resume_string =''.join(resumes_list)
 #     s3_uri = urlparse("s3://ausy-datalake-drift-nonprod/resume-matcher/raw/resume-dataset.csv", allow_fragments=False).geturl()
 #     resumes_list = pd.read_csv(s3_uri, header=None, encoding='utf-8')[0].tolist()
 
@@ -125,9 +132,9 @@ if __name__ == "__main__":
 # def get_skills(resumes: str) -> list:
 
 #     template_resumes_get_skills = """
-#     Given the following string, delimited by <RESUMES> and </RESUMES> which contains resumes which are not properly formatted, categorize the resumes based on domain. 
+#     Given the following string, delimited by <RESUMES> and </RESUMES> which contains resumes which are not properly formatted, categorize the resumes based on domain.
 #     For each domain list the skills of the resumes that are part of that domain.
-    
+
 #     Create a JSON object where they keys are the domains and the values are a list containing the skills.
 
 #     Return that JSON object only.
@@ -155,5 +162,5 @@ if __name__ == "__main__":
 # if __name__ == "__main__":
 #     resumes = get_resumes()
 #     print(resumes)
-    # for x in resumes:
-    #     get_skills(x)
+# for x in resumes:
+#     get_skills(x)