Delete cv_analyzer.py

cv_analyzer.py

@@ -1,76 +0,0 @@
-import json
-import logging
-from cv_prompt import (
-    ResumeQualityEvaluation,
-    get_section_detection_prompt, 
-    get_content_quality_prompt, 
-    calculate_section_detection_score, 
-    calculate_overall_score,
-    get_completeness_prompt
-)
-from openai_utils import get_ai_response
-from ocr_extractor import process_file
-from langchain.output_parsers import PydanticOutputParser
-from spelling_grammar_checker import evaluate_cv_text
-from personal_information import analyze_personal_info
-
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-
-def analyze_cv(file_content):
-    try:
-        extracted_text = process_file(file_content, 'ocr_weights.json')
-        logging.info("Text extracted successfully")
-
-        # Section Detection
-        sections_prompt = get_section_detection_prompt(extracted_text)
-        sections_response = get_ai_response([{"role": "user", "content": sections_prompt}])
-        if sections_response is None:
-            return {"error": "Failed to get AI response for sections"}
-
-        sections_data = json.loads(sections_response)
-        detected_sections = sections_data.get('present_sections', [])
-        section_detection_score = calculate_section_detection_score(detected_sections)
-        logging.info(f"Detected sections: {detected_sections}")
-        logging.info(f"Section detection score: {section_detection_score}")
-
-        # Content Quality Analysis
-        quality_prompt = get_content_quality_prompt(extracted_text)
-        quality_response = get_ai_response([{"role": "user", "content": quality_prompt}])
-        
-        if quality_response is None:
-            return {"error": "Failed to get AI response for content quality"}
-
-        parser = PydanticOutputParser(pydantic_object=ResumeQualityEvaluation)
-        evaluation_result = parser.parse(quality_response)
-        
-        overall_score = calculate_overall_score(evaluation_result)
-
-        # Spelling and Grammar Check
-        spelling_grammar_score = evaluate_cv_text(file_content, 'ocr_weights.json')
-
-        # Personal Information Analysis
-        personal_info = json.loads(analyze_personal_info(file_content))
-
-        # Completeness Analysis
-        completeness_prompt = get_completeness_prompt(extracted_text, detected_sections)
-        completeness_response = get_ai_response([{"role": "user", "content": completeness_prompt}])
-        if completeness_response is None:
-            return {"error": "Failed to get AI response for completeness analysis"}
-        completeness_analysis = json.loads(completeness_response)
-
-        logging.info("All analyses completed")
-        logging.info(f"Overall score: {overall_score}")
-
-        return {
-            "extracted_text": extracted_text,
-            "detected_sections": detected_sections,
-            "section_detection_score": section_detection_score,
-            "content_analysis": evaluation_result.dict(),
-            "overall_score": overall_score,
-            "spelling_grammar_score": spelling_grammar_score,
-            "personal_info": personal_info,
-            "completeness_analysis": completeness_analysis
-        }
-    except Exception as e:
-        logging.error(f"Error in CV analysis: {str(e)}", exc_info=True)
-        return {"error": str(e)}