Update cv_prompt.py

cv_prompt.py

@@ -4,7 +4,6 @@ from langchain_core.pydantic_v1 import BaseModel, Field
 from langchain.output_parsers import PydanticOutputParser
 from langchain_core.prompts import PromptTemplate
 
-
 def load_json_file(filename):
     try:
         with open(filename, 'r', encoding='utf-8') as f:
@@ -52,6 +51,37 @@ class ResumeQualityEvaluation(BaseModel):
     work_experience: WorkExperience = Field(description="Evaluation of the work experience section")
     profile: Profile = Field(description="Evaluation of the profile section")
 
+def get_personal_info_prompt(text):
+    return f"""<s>[INST]Extract the personal information from the following CV text. The text may be in any language. Respond with a JSON object in the format {{"city": {{"extracted city name": true/false}}, "country": {{"extracted country name": true/false}}}}. If you can't find the information, set the value to false.
+
+Text:
+{text}[/INST]"""
+
+def get_spelling_grammar_prompt(text):
+    return f"""<s>[INST]Analyze the following text for spelling and grammar errors. The text may be in any language. Do not correct the errors, just count them. Calculate the percentage of errors.
+
+Text to analyze:
+{text}
+
+Respond with a JSON object containing the key 'error_percentage' with the calculated percentage (0-100) of errors.[/INST]"""
+
+def get_section_detection_prompt(text):
+    if cv_sections is None:
+        return None
+    sections_list = ", ".join(cv_sections['sections'].keys())
+    return f"""<s>[INST] Analyze this CV text and identify which of the following sections are present: {sections_list}. 
+A section is considered present if its content is identifiable, even without an explicit title. 
+Consider synonyms and alternative phrasings for section titles.
+
+Sections to look for:
+{sections_list}
+
+CV text:
+{text}
+
+Respond with a JSON object with a key "present_sections" containing an array of the identified sections. 
+Only include sections that are actually present in the CV. [/INST]"""
+
 def get_content_quality_prompt(text):
     parser = PydanticOutputParser(pydantic_object=ResumeQualityEvaluation)
     
@@ -86,42 +116,6 @@ Provide an overall score for each section on a scale of 0-10 based on the presen
     
     return prompt.format(resume=text)
 
-
-
-
-def get_personal_info_prompt(text):
-    return f"""<s>[INST]Extract the personal information from the following CV text. The text may be in any language. Respond with a JSON object in the format {{"name": "extracted name", "email": "extracted email", "phone": "extracted phone number", "location": "extracted location"}}. If you can't find any of the information, set the value to null.
-
-CV text:
-{text}[/INST]"""
-
-def get_spelling_grammar_prompt(text):
-    return f"""<s>[INST]Analyze the following text for spelling and grammar errors. The text may be in any language. Do not correct the errors, just count them. Calculate the percentage of errors.
-
-Text to analyze:
-{text}
-
-Respond with a JSON object containing the key 'error_percentage' with the calculated percentage (0-100) of errors.[/INST]"""
-
-def get_section_detection_prompt(text):
-    if cv_sections is None:
-        return None
-    sections_list = ", ".join(cv_sections['sections'].keys())
-    return f"""<s>[INST] Analyze this CV text and identify which of the following sections are present: {sections_list}. 
-A section is considered present if its content is identifiable, even without an explicit title. 
-Consider synonyms and alternative phrasings for section titles.
-
-Sections to look for:
-{sections_list}
-
-CV text:
-{text}
-
-Respond with a JSON object with a key "present_sections" containing an array of the identified sections. 
-Only include sections that are actually present in the CV. [/INST]"""
-
-
-
 def calculate_section_detection_score(detected_sections):
     total_score = 0
     for section in detected_sections:
@@ -142,6 +136,4 @@ def calculate_overall_score(evaluation: ResumeQualityEvaluation) -> float:
     
     return round(overall_score, 2)
 
-__all__ = ['ResumeQualityEvaluation', 'get_personal_info_prompt', 'get_spelling_grammar_prompt', 
-           'get_section_detection_prompt', 'get_content_quality_prompt', 
-           'calculate_section_detection_score', 'calculate_overall_score']
+__all__ = ['ResumeQualityEvaluation', 'get_personal_info_prompt', 'get_spelling_grammar_prompt',