refactor

README.md

@@ -2,7 +2,7 @@
 
 Das Programm lädt Stellenangebote der Bundesagentur für Arbeit für 
 ausgewählte Entwickler-Suchbegriffe. 
-Mit **JEV** werden die Anzeigen danach darauf geprüft, 
+Mit **JEV.ai** werden die Anzeigen danach darauf geprüft, 
 ob sie zu meinem TechStack passen. 
 Geeignete Stellenangebote werden anschließend gemeinsam in `stellenangebote.html` 
 ausgegeben.

main.py

@@ -13,32 +13,14 @@ defapi_token = os.getenv("DEF_API_TOKEN")
 arbeitsagentur_base_url = "https://rest.arbeitsagentur.de/"
 defapi_url = "https://api.defapi.org/api/v1/decisions"
 timeout = 60
-
-
+jev_instructions_file = "jev-instructions.json"
 search_strings = ["Developer", "Webentwickler", "Softwareentwickler"]
+wo = "Pforzheim"
 combined_response: JobSearchResponse = {"ergebnisliste": []}
 
-def main():
-    load_dotenv()
-    jobs_data = []
-    for search in search_strings:
-        jobs_data = get_jobs(search)
-        combined_response["ergebnisliste"].extend(jobs_data["ergebnisliste"])
 
-    job_refs = get_job_refs(combined_response)
-    jev_instructions = read_json()
-    print(jobs_data)
-    html_parts = []
-    for job_ref in job_refs:
-        data = classify(job_ref, jev_instructions)
-        if data:
-            html_parts.append(html_helper.render_html(data))
-
-    html_helper.write_html(html_parts)
-
-
-def read_json() -> dict:
-    with open("jev-instructions.json", mode="r", encoding="utf-8") as read_file:
+def read_json(file_name) -> dict:
+    with open(file_name, mode="r", encoding="utf-8") as read_file:
         jev_instructions = json.load(read_file)
         read_file.close()
         return jev_instructions
@@ -61,7 +43,7 @@ def get_jobs(search_string: str) -> JobSearchResponse:
     params = {
         "angebotsart": "1",
         "was": search_string,
-        "wo": "Pforzheim",
+        "wo": wo,
         "umkreis": "50",
         "arbeitszeit": "ho;vz;tz",
         "page": "1",
@@ -99,7 +81,7 @@ def classify(job_ref: str, jev_instructions: dict) -> tuple[Stellenangebot, floa
         "model": "typesafe/jev-1.13",
         "state": job_data["stellenangebotsBeschreibung"],
         "questions": {
-            "job_validation": jev_instructions
+            "job_classification": jev_instructions
         }}
 
     response = requests.post(defapi_url, headers=headers, json=data, timeout=timeout)
@@ -107,7 +89,7 @@ def classify(job_ref: str, jev_instructions: dict) -> tuple[Stellenangebot, floa
     if response.status_code == 200:
         response_data = response.json()
         answers = response_data["answers"]
-        score = answers["job_validation"]["noul"]
+        score = answers["job_classification"]["noul"]
         if score > 0.6:
             return job_data, score
         else:
@@ -117,6 +99,19 @@ def classify(job_ref: str, jev_instructions: dict) -> tuple[Stellenangebot, floa
         return None
 
 
+def main():
+    for search in search_strings:
+        jobs_data = get_jobs(search)
+        combined_response["ergebnisliste"].extend(jobs_data["ergebnisliste"])
+
+    jev_instructions = read_json(jev_instructions_file)
+    html_parts = []
+    for job_ref in get_job_refs(combined_response):
+        data = classify(job_ref, jev_instructions)
+        if data:
+            html_parts.append(html_helper.render_html(data))
+
+    html_helper.write_html(html_parts)
 
 
 if __name__ == "__main__":

modules/html_helper.py

@@ -1,8 +1,8 @@
 import json
-
 import markdown
 from tinyhtml import h, raw, render
-from stellenangebot import Stellenangebot
+from modules.stellenangebot import Stellenangebot
+
 
 def render_html(data: tuple[Stellenangebot, float]) -> str:
     job_data, score = data