Merge pull request #2 from drift-ai/feature/test_at_scale

Pipfile

@@ -8,6 +8,9 @@ gradio = "*"
 requests = "*"
 openai = "*"
 langchain = "*"
+boto3 = "*"
+utils = "*"
+s3fs = "*"
 
 [dev-packages]
 

Pipfile.lock

@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
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+            "sha256": "baada24f686970aa797e81ab57198eff4adaa42274331ba31f06e107dacc04af"
         },
         "pipfile-spec": 6,
         "requires": {
@@ -133,14 +133,6 @@
             "markers": "python_version >= '3.7'",
             "version": "==5.0.1"
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-        "annotated-types": {
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-            "version": "==0.5.0"
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@@ -154,7 +146,7 @@
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-            "markers": "python_version < '3.11'",
+            "markers": "python_version >= '3.6'",
             "version": "==4.0.2"
         },
         "attrs": {
@@ -165,13 +157,29 @@
             "markers": "python_version >= '3.7'",
             "version": "==23.1.0"
         },
+        "boto3": {
+            "hashes": [
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+            "index": "pypi",
+            "version": "==1.28.10"
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+        "botocore": {
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+            "version": "==2023.7.22"
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@@ -323,14 +331,6 @@
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             "version": "==0.5.13"
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@@ -355,43 +355,43 @@
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         "frozenlist": {
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@@ -524,14 +524,6 @@
             "markers": "python_version >= '3.5'",
             "version": "==3.4"
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@@ -540,6 +532,14 @@
             "markers": "python_version >= '3.7'",
             "version": "==3.1.2"
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+        "jmespath": {
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@@ -632,19 +632,19 @@
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@@ -917,37 +917,34 @@
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+                "sha256:791f409064d0a69dd20579345d852c59822c6aa087f23b07b1b4e28ff5880fcb",
+                "sha256:9a3a9f3a61480cc086117b426a8bd86869c213fc4072e606f01c4e4b66eb92bf",
+                "sha256:c1516db588987450b85595586605742879e50dcce923e8973f79529651545b57",
+                "sha256:c40571fe966393b212689aa17e32ed905924120737194b5d5c1b20b9ed0fb171",
+                "sha256:d412c1697c3853c6fc3cb9751b4915859c7afe6a277c2bf00acf287d56c4e625",
+                "sha256:d5154b1a25ec796b1aee12ac1b22f414f94752c5f94832f14d8d6c9ac40bcca6",
+                "sha256:d736b75c3f2cb96843a5c7f8d8ccc414768d34b0a75f466c05f3a739b406f10b",
+                "sha256:e8f6049c4878cb16960fbbfb22105e49d13d752d4d8371b55110941fb3b17800",
+                "sha256:f76aebc3358ade9eacf9bc2bb8ae589863a4f911611694103af05346637df1b7",
+                "sha256:fd67b306320dcadea700a8f79b9e671e607f8696e98ec255915c0c6d6b818503"
+            ],
+            "markers": "python_version >= '3.9'",
+            "version": "==1.25.1"
         },
         "openai": {
             "hashes": [
@@ -1118,14 +1115,6 @@
             "markers": "python_version >= '3.8'",
             "version": "==10.0.0"
         },
-        "pkgutil-resolve-name": {
-            "hashes": [
-                "sha256:357d6c9e6a755653cfd78893817c0853af365dd51ec97f3d358a819373bbd174",
-                "sha256:ca27cc078d25c5ad71a9de0a7a330146c4e014c2462d9af19c6b828280649c5e"
-            ],
-            "markers": "python_version < '3.9'",
-            "version": "==1.3.10"
-        },
         "pydantic": {
             "hashes": [
                 "sha256:008c5e266c8aada206d0627a011504e14268a62091450210eda7c07fabe6963e",
@@ -1168,113 +1157,6 @@
             "markers": "python_version >= '3.7'",
             "version": "==1.10.11"
         },
-        "pydantic-core": {
-            "hashes": [
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-                "sha256:ed5babdcd3d052ba5cf8832561f18df20778c7ccf12587b2d82f7bf3bf259a0e",
-                "sha256:eda1a89c4526826c0a87d33596a4cd15b8f58e9250f503e39af1699ba9c878e8",
-                "sha256:ef1fd1b24e9bcddcb168437686677104e205c8e25b066e73ffdf331d3bb8792b",
-                "sha256:ef6a222d54f742c24f6b143aab088702db3a827b224e75b9dd28b38597c595fe",
-                "sha256:f3dd5333049b5b3faa739e0f40b77cc8b7a1aded2f2da0e28794c81586d7b08a",
-                "sha256:f60e31e3e15e8c294bf70c60f8ae4d0c3caf3af8f26466e9aa8ea4c01302749b",
-                "sha256:f642313d559f9d9a00c4de6820124059cc3342a0d0127b18301de2c680d5ea40",
-                "sha256:f868e731a18b403b88aa434d960489ceeed0ddeb44ebc02389540731a67705e0",
-                "sha256:f93c867e5e85584a28c6a6feb6f2086d717266eb5d1210d096dd717b7f4dec04"
-            ],
-            "markers": "python_version >= '3.7'",
-            "version": "==2.3.0"
-        },
         "pydub": {
             "hashes": [
                 "sha256:65617e33033874b59d87db603aa1ed450633288aefead953b30bded59cb599a6",
@@ -1282,14 +1164,6 @@
             ],
             "version": "==0.25.1"
         },
-        "pygments": {
-            "hashes": [
-                "sha256:8ace4d3c1dd481894b2005f560ead0f9f19ee64fe983366be1a21e171d12775c",
-                "sha256:db2db3deb4b4179f399a09054b023b6a586b76499d36965813c71aa8ed7b5fd1"
-            ],
-            "markers": "python_version >= '3.7'",
-            "version": "==2.15.1"
-        },
         "pyparsing": {
             "hashes": [
                 "sha256:2b020ecf7d21b687f219b71ecad3631f644a47f01403fa1d1036b0c6416d70fb",
@@ -1486,6 +1360,22 @@
             "markers": "python_version >= '3.8'",
             "version": "==0.9.2"
         },
+        "s3fs": {
+            "hashes": [
+                "sha256:2ca5de8dc18ad7ad350c0bd01aef0406aa5d0fff78a561f0f710f9d9858abdd0",
+                "sha256:91c1dfb45e5217bd441a7a560946fe865ced6225ff7eb0fb459fe6e601a95ed3"
+            ],
+            "index": "pypi",
+            "version": "==0.4.2"
+        },
+        "s3transfer": {
+            "hashes": [
+                "sha256:3c0da2d074bf35d6870ef157158641178a4204a6e689e82546083e31e0311346",
+                "sha256:640bb492711f4c0c0905e1f62b6aaeb771881935ad27884852411f8e9cacbca9"
+            ],
+            "markers": "python_version >= '3.7'",
+            "version": "==0.6.1"
+        },
         "semantic-version": {
             "hashes": [
                 "sha256:bdabb6d336998cbb378d4b9db3a4b56a1e3235701dc05ea2690d9a997ed5041c",
@@ -1622,18 +1512,24 @@
         },
         "urllib3": {
             "hashes": [
-                "sha256:8d22f86aae8ef5e410d4f539fde9ce6b2113a001bb4d189e0aed70642d602b11",
-                "sha256:de7df1803967d2c2a98e4b11bb7d6bd9210474c46e8a0401514e3a42a75ebde4"
+                "sha256:8d36afa7616d8ab714608411b4a3b13e58f463aee519024578e062e141dce20f",
+                "sha256:8f135f6502756bde6b2a9b28989df5fbe87c9970cecaa69041edcce7f0589b14"
             ],
-            "markers": "python_version >= '3.7'",
-            "version": "==2.0.4"
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4, 3.5'",
+            "version": "==1.26.16"
+        },
+        "utils": {
+            "hashes": [
+                "sha256:ff04549b95deb2fd7a82ddaea077e2f41209079e2482df231c358770b27b72a7"
+            ],
+            "index": "pypi",
+            "version": "==1.0.1"
         },
         "uvicorn": {
             "hashes": [
                 "sha256:1d55d46b83ee4ce82b4e82f621f2050adb3eb7b5481c13f9af1744951cae2f1f",
                 "sha256:da9b0c8443b2d7ee9db00a345f1eee6db7317432c9d4400f5049cc8d358383be"
             ],
-            "index": "pypi",
             "markers": "python_version >= '3.8'",
             "version": "==0.23.1"
         },
@@ -1792,14 +1688,6 @@
             ],
             "markers": "python_version >= '3.7'",
             "version": "==1.9.2"
-        },
-        "zipp": {
-            "hashes": [
-                "sha256:679e51dd4403591b2d6838a48de3d283f3d188412a9782faadf845f298736ba0",
-                "sha256:ebc15946aa78bd63458992fc81ec3b6f7b1e92d51c35e6de1c3804e73b799147"
-            ],
-            "markers": "python_version < '3.10'",
-            "version": "==3.16.2"
         }
     },
     "develop": {}

app.py

@@ -2,7 +2,6 @@ import json
 import os
 import gradio as gr
 import requests
-import intro
 import recruiting_assistant
 
 
@@ -103,7 +102,7 @@ with demo:
              outputs=[text_intro, evaluation]
     )
 
-    examples = gr.Examples(
+    gr.Examples(
         examples=examples,
         fn=search_resume,
         inputs=text_vacancy,

recruiting_assistant.py

@@ -171,9 +171,26 @@ def create_intro(vacancy=vacancy, resume=resume):
 
     result = match_resume_vacancy_skills_chain({"vacancy": vacancy, "resume": resume})
     print(result)
-    return result["introduction_email"], json.dumps({"resume skills":result['resume_skills'], "check_past_experiences": result['check_past_experiences']}, indent=4)
 
-    
+    resume_skills = json.loads(result['resume_skills'])
+    relevant_skills = len(resume_skills["skills_present"])
+    total_skills = len(resume_skills["skills_present"] + resume_skills["skills_not_present"])
+    score_skills = round(100.0*(relevant_skills/total_skills),2)
+
+
+    check_past_experiences = json.loads(result['check_past_experiences'])
+    relevant_experiences = len(check_past_experiences["relevant_experiences"])
+    total_experiences = len(check_past_experiences["relevant_experiences"] + check_past_experiences["irrelevant_experiences"])
+    score_experiences = round(100.0*(relevant_experiences/total_experiences),2)
+
+    new_line = '\n'
+
+    score = f"""
+    Skills (Score: {score_skills}%)
+    Relevant Skills: {",".join(resume_skills["skills_present"])}
+    Not Relevant Skills: {",".join(resume_skills["skills_not_present"])}    
+    """
+    return result["introduction_email"], score
 
 if __name__ == '__main__':
-    create_intro(vacancy=vacancy,resume=resume)
+    create_intro(vacancy=vacancy,resume=resume)

test.py

@@ -0,0 +1,166 @@
+import boto3
+import os
+import json
+import pandas as pd
+from urllib.parse import urlparse
+import random
+
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts import ChatPromptTemplate
+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"
+    )
+
+    generate_skills_chain = SequentialChain(
+        chains=[role_skills],
+        input_variables=[],
+        output_variables=["role_skills"],
+        verbose=False,
+    )
+
+    result = generate_skills_chain({})
+    result_array = json.loads(result["role_skills"])["skills"]
+    return result_array
+
+
+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.
+
+    ```
+    {skills}
+    ```
+    """
+
+    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,
+    )
+
+    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.
+
+    ```
+    {resume}
+    ```
+    """
+
+    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,
+    )
+
+    result = retrieve_skills_chain({"resume": resume})
+    result_array = json.loads(result["skills"])
+
+    return result_array
+
+
+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)
+
+
+if __name__ == "__main__":
+    role_skills = generate_skills()
+    random_skills = random.sample(role_skills, 3)
+    resume = generate_resume(random_skills)
+    skills = retrieve_skills(resume)
+    score = get_score(random_skills, skills)
+    print(random_skills)
+    print(skills)
+    print(score)
+
+# def get_resumes() -> str:
+
+#     s3 = boto3.client(
+#         '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)
+#     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()
+
+#     return resumes_list
+
+# 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.
+#     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.
+
+#     <RESUMES>
+#     {resumes}
+#     </RESUMES>
+#     """
+
+#     prompt_vacancy_get_skills = ChatPromptTemplate.from_template(template=template_resumes_get_skills)
+#     resume_skills = LLMChain(llm=llm, prompt=prompt_vacancy_get_skills, output_key="resume_skills")
+
+#     get_skills_resumes_chain = SequentialChain(
+#         chains=[resume_skills],
+#         input_variables=["resumes"],
+#         output_variables=["resume_skills"],
+#         verbose=False
+#     )
+
+#     result = get_skills_resumes_chain({"resumes": resumes})
+#     # print(result)
+#     resume_skills = json.loads(result['resume_skills'])
+#     print(resume_skills)
+
+# if __name__ == "__main__":
+#     resumes = get_resumes()
+#     print(resumes)
+# for x in resumes:
+#     get_skills(x)