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OOE "MH i %" I KB Kr"# K  =>a!   $*E !  ' *8*D C K M NNCE7K L M# K!DRDIJ K6 ! H L  !!XXh'!1v "  \\^i&:: ,XJoj\QXYZX[ \  !!XXh'!$1v "   GzQXYZX[\] EhZwWXVY Z  !!!1v "  \\^i&::  ,XJ7'Ls 4  ,XJ7'Ls 4 !!!$1v " -sdCC<AD#!C98C9H L"AJ LBLLc t|jddjddjdd}|jdsd|z}d|d|d}tjd d } t j tj||d }|jd k(r<|j}|jddurtjd|yt|j#t$rt}tj t#dz}t$j&j)||dd|t|tj+d|d|Yd}~yd}~wwxYw)N+ -91qenglish)routemessagelanguagenumbersapplication/json) authorization Content-Type )jsonheaderstimeoutreturnTz[SMS] SENT to=rOrPrSr )to_phonerqrLrMrTrNz[SMS] QUEUED to=rRF)rVr) startswithrFAST2SMS_API_KEYrequestspost FAST2SMS_URL status_coderxgetrXr]r_r&rr`rrr=r>rY)r}rqpayloadryres response_datarerfs r. send_sms_safers` H b  b  b      t $(? G"22*G mm  ! !   ??c !HHJM  *d2 nXJ78!!  \\^i&:: $1v   )(71#>? s -A1C4C44 E1=A*E,,E1ctjtjzdjfdt |D}|S)Nrjc3HK|]}tjywN)randomchoice).0_ characterss r. z$generate_password..FsL6==4Ls")string ascii_lettersdigitsr^range)lengthpasswordrs @r.generate_passwordrAs6))FMM9 77LeFmLLr0c@tjd|jS)Nz/apply/)rCAREERapply_token_urljds r.generate_apply_linkrJsoo gb&8&8%9 ::r0ct|d}d|}d|dd}tj||}|jdk(S) Nrj)safez%https://api.linkedin.com/v2/ugcPosts/zBearer z2.0.0) AuthorizationzX-Restli-Protocol-Version)ry)rrdeleter) access_tokenpost_urn encoded_urnurlryrs r.delete_linkedin_postrMsQr*K 1+ ?C#<.1%,G //#w /C ??c !!r0cd}dd|jd}|j|j||j|jd}ddt |j zdS) Nz"https://api.foundit.in/v1/job/postrtzBasic )rvr)username job_titlejob_descriptionlocationexperience_minsuccessFI_)rLfoundit_job_id)portal_passwordportal_usernamer job_locationyears_of_experiencerVpk)configrr&rryrs r.post_to_founditrYsj .C+!&"8"8!9:G **\\OO00 G 53ruu:3E FFr0c$eZdZdZdZdZdZy) LlmPromptscd|dS)Na Role: Act as an Expert HR Data Parser and Resume Analyst. Input Data: I will provide you with the raw text from an EMAIL BODY containing a candidate's resume or profile. The text may include email headers, signatures, forwarding history, and conversational text (e.g., "Please find attached..."). Your Task: Noise Filtering: actively ignore email metad ata, "Sent from my iPhone" footers, legal disclaimers, and conversational introductions. Isolate the actual candidate profile data. Semantic Analysis: Standardization: transfer the specific to given JSON format. Format: Output the data strictly into the JSON format defined below. Rules for Extraction: Name: Identify the candidate's name, not the recruiter's or sender's name. Age : Calculate the age from the date of birth, blank if date for birth is not specified Gender : Gender of the candidate either Male or FeMale, blank if not gende is not available Employment type : Current Job type like Contract or Employee or Part Time or Freelancer, leave blank if not available Dates: Ensure dates are formatted consistently (e.g., "Month Year" or "Year"). Title: Identify from the resume. if resume doesn't have the title, create a suitable title for the resume. Primary Skills: more experience skills Responsibility Summary: summary of all the skills, work experience responsibilities and project experience. Domain Skills: Industry or Buisiness Related skills Missing Data: If a specific field is not found, return "Not Specified" (do not use null or empty strings). Output Format: Return ONLY a single valid JSON object. Do not include any conversational text. ```json { "personal_information": { "first_name": "String", "last_name" : "String", "full_name": "String", "email": "String", "mobile": "String", "linkedin_url": "String", "portfolio_url": "String", "location": "String", "age": "String", "gender": "String", "notice_period": "String", "empoyment_type" }, "title": "String", "total_exp": "String", "salary" : "String", "professional_summary": "String", "responsibility_summary": "String', "primary_skills": ["String"], "skills": { "technical_skills": ["String"], "soft_skills": ["String"], "tools_and_frameworks": ["String"] }, "domain_skills": ["String"], "work_experience": [ { "job_title": "String", "company": "String", "location": "String", "start_date": "String", "end_date": "String", "is_current": Boolean, "responsibilities": "String" } ], "education": [ { "degree": "String", "institution": "String", "location": "String", "graduation_year": "String" } ], "projects": [ { "project_name": "String", "description": "String", "technologies_used": ["String"] } ], "certifications": [ { "name": "String", "issuer": "String", "year": "String" } ], "languages": ["String"] } ``` Input Email Body: ) resume_texts r.resume_extraction_promptz#LlmPrompts.resume_extraction_promptns"X pM qY Y r0cd|d}|S)NaQ Role: Act as an Expert Technical Recruiter and Data Parsing AI. Input Data: I will provide you with the raw text from an EMAIL BODY or PDF or Word, which containing an unstructured Job Description (JD). Your Task: Analyze: Read the text and identify key job details. Ignore email metadata (signatures, "Sent from my iPhone", greetings, and thread headers). Semantic Extraction & Tuning: Interpret Meaning: If the email says, "looking for a rockstar in Python," interpret this as "Expert proficiency in Python" under skills. Standardize: Expand only universal recruitment abbreviations (e.g., "wfh" -> "Remote"). Strictly preserve Client Acronyms, Project Codes, and Proper Nouns exactly as written (e.g., keep "LTA", "JPMC", "DBS" as is; do not expand them). Categorize: Semantically distinguish between what is "Required" (Must have) vs. "Preferred" (Nice to have/Bonus). Refine: Clean up the grammar and tone of the extracted text to be professional and suitable for a formal JD. Audit Transformation: Track every specific change made, including which input headers were mapped to which output keys. Format: Output the data strictly into the specific JSON format defined below. Rules for Extraction: If a field is not mentioned in the email, return "Not Specified". jd_date: Extract the date from the email text if present, otherwise use "Current". job_title : Retain the given title in the JD ai_title : Tuned and Generated Title from the JD job_type: type of Job in the JD example Contract or Employee or etc.. job_id: reference for the JD or JD identifier duration : Duration of the Job requirement in months, if not specified use 24+ months about_company: If not explicitly stated, try to infer the company name/domain from the context, or leave as "Not Specified". no_of_open_positions: If not explicitly stated, give default value as 1 primary_skills: highly important skills required for the JD secondary_skills: technical skills exclude the primary skills domain_requirements: must specified as domain skills in the JD Specific Instruction for "changes" field: You must generate a semicolon-separated string listing THREE types of changes: MAPPING: Which input section was mapped to which JSON key (e.g., "Mapped 'Key Responsibilities' to 'responsibilities'"). CORRECTION: Content fixes (e.g., "Corrected '58 years' to '5-8 Years'"). INFERENCE: Data derived from context (e.g., "Inferred 'LTA' is the Domain/Client"). Output Format: Return ONLY a single valid JSON object. Do not include any conversational text. ```json { "ai_title" : "String", "job_title" : "String", "job_type" : "String", "job_id" : "String", "about_company" : "String", "no_of_open_positions" : "String", "job_summary" : "String", "responsibilities" : "String", "domain_requirements" : "String", "certification_requirements" : "String", "security_clearance_requirements" : "String", "years_of_experience" : "String", "duration" : "String", "onsite_job" : "String", "job_location" : "String", "salary_range" : "String", "required_qualifications" : "String", "preferred_qualifications" : "String", "working_hours" : "String", "benefits" : "String", "requirement_priority" : "String", "search_pattern" : { "job_title": "String", "location": "String", "education": ["String"], "primary_skills": ["String"], "secondary_skills": ["String"], "soft_skills": ["String"], "tools_and_frameworks": ["String"], "salary_range": "String", "keywords": ["String"], "domain_requirements": ["String"] }, "changes": "String", } ``` Input Email Body: rr)final_body_textjd_generate_prompts r.jd_extraction_promptzLlmPrompts.jd_extraction_prompts)J" T  UK X"!r0cd|dS)Na Role: Act as an Expert Technical Recruiter and Data Parsing AI. Input Data: I will provide you with the JSON text, which containing list of structured Job Description (JD). Your Task: Analyze: Read each JD in the JSON and generate the search patterns in specific JSON structure Format: Output the data strictly into the specific JSON format defined below. Rules for Extraction: jd_id : Retain the given jd_id in the JD job_title : Retain the given title in the JD primary_skills: highly important skills required for the JD secondary_skills: technical skills exclude the primary skills domain_requirements: must specified as domain skills in the JD Output Format: Return list of valid JSON object. Do not include any conversational text. ```json [ { "jd_id" : "String", "search_pattern" : { "job_title": "String", "location": "String", "education": ["String"], "primary_skills": ["String"], "secondary_skills": ["String"], "soft_skills": ["String"], "tools_and_frameworks": ["String"], "salary_range": "String", "keywords": ["String"], "domain_requirements": ["String"] } } ] ``` Input JSON: rr)rs r.jd_search_pattern_promptz#LlmPrompts.jd_search_pattern_prompts % J  K& & r0cd|dS)Nz` Generate a structured JSON report for this interview. Interview Logs: a Output Format: ``` json { "Candidate_Strengths" : "String[]", "Candidate_Weaknesses": "String[]", "Score": { "communication": 'String", "attitude": "String", "Technical": "String" }, "Final_Hiring_Recommendation": "String (Excellent/Very Good/Good/Fair/Poor)" } ``` r)interview_datas r.l0_interview_summary_promptz&LlmPrompts.l0_interview_summary_promptBs+,-   r0N)__name__ __module__ __qualname__rrrrrr0r.rrmsZ xM"^' R r0rr!r|c|sy|jj}tjdd|}tjdd|}tjdd|j}|S)Nrjz[_\-\/]+rkz [^a-z0-9 ]+\s+)striplowerresub)r!s r.normalize_resume_titlerVsb  KKM   !E FF;U +E FF>2u -E FF63 & , , .E Lr0cbtjd|}tjd|}tjd||}g}g}|j D]3\}}} } } dj ||| } dj || | } |dk(rI|j tj| |j tj| |dk(rQ|j dtj| d|j dtj| d|d k(r*|j dtj| d|d k(s |j dtj| d6dj |dj |fS) z Returns (old_html, new_html) old_html: highlights removed/changed words (blue) new_html: highlights added/changed words (green) z\w+|\s+|[^\w\s]Nrjequalr)zDzzDrinsert) rfindalldifflibSequenceMatcher get_opcodesr^rWr$escape)old_linenew_line old_tokens new_tokenssm old_result new_resulttagi1i2j1j2old_partnew_parts r.highlight_inline_diffrhs.9J.9J  z: >BJJ!~~/ERR77:b,-77:b,- '>   dkk(3 4   dkk(3 4 I     deiepepqyezd{|CD E    deiepepqyezd{|CD E H_    deiepepqyezd{|CD E H_    deiepepqyezd{|CD E!E$ 77:  3 33r0cFdd|jd|jd|jg}|jr|j d|jt |j }|jd|d|j gdj||fS) Nu🚀 We are Hiring!u 🔹 Role: u📍 Location: u🧠 Experience: u 💰 Salary: u👉 Apply here: u 🆔 Ref ID: r') rrr salary_rangerWr jd_display_idextendr^)rlines apply_urls r.build_facebook_post_textrs bll^$ "//*+ B2234  E  }R__$567#B$4$45I LL I;' (()* ;;u y ((r0title1title2c|r|sytd}|j||g}t|dd|dddd}t|dzdS)Ngro) stop_wordsrr d)r fit_transformrround)rr vectorizer tfidf_matrix similaritys r.calculate_title_similarityrs_  I6J++VV,<=L"<!#4l1Q6GHKANJ c!1 %%r0c|r|sy|j}|j}t|dkst|dkryt||S)N)rlenr)ai_titlers r.safe_similarityrsI 9~~H!I 8}qC NQ. %h ::r0ctjjdtjj|j ddj S)z@ Returns Delivery Managers assigned to the given client zDelivery Manager (DM)clientuser_idTflat) groups__nameid__in)rr=filterr values_listdistinctrs r.get_client_delivery_managersr sV <<  ,!!(() +id+ +   hj r0c |D]}|jstdd\}}|j|}|j|jxsdd|jxsdj |||}t ||j |jtjdy y#t$r}td |Yd}~yd}~wwxYw) Nai_title_mismatch_notificationFr+)rrjrk)dm_namerrrr-r"rKrDr+TEmail unexpected error:) rcr/format first_name last_namerrgrEMAIL_HOST_USERr_r<) dm_usersrrrrdmr-r"res r.notify_dm_low_matchrs B88./OMGTnn%%G;;==.B/q1C0DEKKM+#! 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