o GY)jw@sdZddlZddlZddlZddlmZmZmZddlmZddl Z ddl m Z ddl m Z ddlmZddlmZmZeeZe d d Ze d Ze d Ze d dZdZGdddZddededefddZdS)zv Call Analysis with Quality Parameters Uses AWS Nova AI to analyze calls against business-specific quality parameters N)DictListOptional)datetime)QualityParametersHandler)calculate_talk_listen_ratio)DatabaseHandler)load_summary_pipeline_configresolve_summary_prompt_parts AWS_REGIONz us-east-1AWS_ACCESS_KEY_IDAWS_SECRET_ACCESS_KEYAWS_NOVA_MODELzamazon.nova-lite-v1:0c @sBeZdZdZddZdeedefddZdd d e ed e de fd d Z d&dedeed e edefddZ d&ded e edefddZdeede ede efddZdedefddZdedefddZd edededeefdd Zd&d ed!ededeededef d"d#Zd&d ed!ededeededef d$d%ZdS)' CallAnalyzerzFAnalyze calls using AWS Nova with business-specific quality parameterscCs2||_t||_t||_tjdttt d|_ dS)Nzbedrock-runtime) service_name region_nameaws_access_key_idaws_secret_access_key) configrquality_params_handlerr db_handlerboto3clientr r r bedrock_runtime)selfrrH/home/aiteam/pcaa-dev/dashboard-backend/analyze_calls_with_parameters.py__init__s   zCallAnalyzer.__init__ parametersreturncCs|g}|D]4}d|dd|dd|dd|dd |d d |d p%d d|dd|dd}||qd|S)z'Format quality parameters for AI promptz **Parameter:** parameter_namez - **Category:** parameter_groupz - **Max Score:** max_scorez - **What to check:** check_descriptionz - **Description:** detailed_descriptionz - **Sample utterances:** sample_utteranceszN/Az - **Auto-detect N/A:** auto_detect_naz - **Type:** parameter_type )appendjoin)rr formattedparam param_strrrrformat_parameters_for_prompt,s*  z)CallAnalyzer.format_parameters_for_promptThas_quality_parametersbidr1cCs |rt|j|nd}t||dS)Nr0)r rr )rr2r1 pipeline_cfgrrr_summary_parts_for_bid?sz#CallAnalyzer._summary_parts_for_bidN transcriptc CsP||}||\}}|rd|d}nd}d|d|d|d|d }|S)z/Build comprehensive AI prompt for call analysisr) zYou are an expert call quality analyst. Analyze the following call transcript against specific quality parameters. CALL TRANSCRIPT: z" QUALITY PARAMETERS TO EVALUATE: a INSTRUCTIONS: For EACH parameter above: 1. **Determine Applicability:** Decide if this parameter applies to this specific call. Be generous - only mark as "not applicable" if the parameter truly cannot be evaluated for this call type. Not-applicable parameters count as 0 points toward the overall quality score (their max_score still counts in the denominator). 2. **Score (if applicable):** If the parameter is applicable, score it as an integer from 0 up to max_score (inclusive). - The score must be on the same scale as max_score (e.g. if max_score is 10, valid scores are 0-10, NOT 0-100). - **DEFAULT: Give FULL max_score unless there are clear problems** - If the agent made partial effort, deduct proportionally on the 0..max_score scale (e.g. 7/10, not 70/10). - Only deduct points for serious, obvious failures or complete absence of the behavior - When in doubt, give the higher score - be generous and supportive 3. **Provide Evidence:** - Exact transcript segment where you detected this (quote the relevant lines) - Approximate speaker turn number (counting from the start of the conversation) - Approximate timestamp in format "MM:SS" 4. **Reasoning:** Brief explanation of why you gave this score or why it's not applicable. aCUSTOMER PROFILING (BANT): Also extract sales qualification details for Budget, Authority, Need, and Timeline. - Provide a clear value if mentioned (amount, range, or statement). - If not mentioned, set value to "Not mentioned" and evidence to "N/A". - Include evidence and reasoning for each field. OUTPUT FORMAT (JSON only, no additional text): { "parameters": { "Parameter Name": { "applicable": true/false, "score": , "max_score": , "detected_in_segment": "", "speaker_turn": , "timestamp": "MM:SS", "reasoning": "" } }, "customer_profile": { "budget": { "value": "", "evidence": "", "reasoning": "", "confidence": "high/medium/low" }, "authority": { "value": "", "evidence": "", "reasoning": "", "confidence": "high/medium/low" }, "need": { "value": "", "evidence": "", "reasoning": "", "confidence": "high/medium/low" }, "timeline": { "value": "", "evidence": "", "reasoning": "", "confidence": "high/medium/low" } }, "customer_profile_summary": "<1-2 sentence summary of BANT status>", "overall_summary": "a", "call_purpose": "
", "objections_concerns": "", "objection_type": "<2-3 word classification of the objection/concern type, e.g., 'Delivery Issue', 'Food Quality', 'Payment Problem', 'Order Delay', 'Wrong Items', 'Bad Service', 'None' if no objection>", "sentiment": "" } IMPORTANT: - Return ONLY valid JSON, no markdown formatting - Use exact parameter names as keys in the "parameters" object - For N/A parameters, set "applicable": false and provide a reason - Provide actual transcript quotes for "detected_in_segment" - Be precise with timestamps and speaker turns - For customer_profile fields, always include value, evidence, reasoning, and confidence )r/r4) rr5rr2params_formattedsummary_config_blockoverall_summary_hintsummary_sectionpromptrrrbuild_analysis_promptGs GWz"CallAnalyzer.build_analysis_promptcCs>|j|dd\}}|rd|dnd}d|d|d|dS)zFBuild a prompt for short calls where quality scoring is not applicableFr0r)r6zcAnalyze the following short call transcript for sales qualification and summary. CALL TRANSCRIPT: zGOUTPUT FORMAT (JSON only, no additional text): { "overall_summary": "a", "call_purpose": "
", "sentiment": "", "customer_profile": { "budget": { "value": "", "evidence": "", "reasoning": "", "confidence": "high/medium/low" }, "authority": { "value": "", "evidence": "", "reasoning": "", "confidence": "high/medium/low" }, "need": { "value": "", "evidence": "", "reasoning": "", "confidence": "high/medium/low" }, "timeline": { "value": "", "evidence": "", "reasoning": "", "confidence": "high/medium/low" } }, "customer_profile_summary": "<1-2 sentence summary of BANT status>" } IMPORTANT: - Return ONLY valid JSON, no markdown formatting )r4)rr5r2r8r9r:rrrbuild_short_call_prompts z$CallAnalyzer.build_short_call_promptspeaker_segmentsactual_durationc Cs|durzt|WSttfyYdSw|sdSz*g}|D]}|d}|dur/|d}|dur:|t|q|rBt|WSdWSttfyPYdSw)zAGet duration in seconds from actual duration or speaker segments.Nend_timeend)float TypeError ValueErrorgetr*max)rr>r? end_timessegend_valrrr_get_duration_secondss*   z"CallAnalyzer._get_duration_secondssentiment_valuecCsz|durdSt|ts dS|sdS|}d|vr#|d}d|vs+d|vr-dSd|vs9d|vs9d |vr;dSdS) z:Normalize sentiment to one of: positive, negative, neutralNneutral rpositivegoodnegativebadpoor) isinstancestrstriplowersplit)rrKsentiment_lowerrrr_normalize_sentiments   z!CallAnalyzer._normalize_sentimentr;c szdd|igdgdddd}tdt|jjtd d t|d }|d d }t |}| d i di dg}|rSt |t rS|d dd }ntdtd|dddddlfdd}||} zt | WWStjy} ztd| td| d} ~ wwtjy} ztd| td|d} ~ wty} z td| d} ~ ww)zCall AWS Nova Bedrock APIusertext)rolecontentg333333?i ) temperaturemax_new_tokens)messagesinferenceConfigzCalling AWS Nova model: zapplication/json)modelId contentTypeacceptbodyrezutf-8outputmessager]rz,Empty content received from Bedrock responsezReceived response from Nova: Nz...csd|j}|r|d}n!|d}|d}|dkr/|dkr/||kr/|||d}n|}dd|}dd |}d d |}|S) Nz```(?:json)?\s*(\{.*?\})\s*```{}z ,\s*([\]}])z\1z+("speaker_turn"\s*:\s*)(\d+(?:\s*,\s*\d+)+)z\1[\2]z&("speaker_turn"\s*:\s*)(\d+\s*-\s*\d+)z\1"\2")searchDOTALLgroupfindrfindsub)r[matchjson_str start_idxend_idxrerrextract_json_from_text6s(   z:CallAnalyzer.call_aws_nova..extract_json_from_textzFailed to parse cleaned JSON: zCleaned string attempts: z+Failed to parse AWS Nova response as JSON: zResponse text: zError calling AWS Nova: )loggerinforr invoke_modeljsondumpsreaddecodeloadsrErSlistrUrDryJSONDecodeErrorerror Exception) rr; request_bodyresponse raw_response response_body content_list response_textrzclean_json_strnested_eerrxr call_aws_nova sX  zCallAnalyzer.call_aws_novaobjections_textc Cs|r|dkr gSzS|j}|}|d|f|}Wdn1s*wY|s|sz5CallAnalyzer._classify_objections..z_You are analyzing customer objections from a call transcript. OBJECTIONS/CONCERNS IDENTIFIED: z! CALL TRANSCRIPT (for context): z" AVAILABLE OBJECTION CATEGORIES: aa TASK: Match each objection/concern to the most appropriate category ID from the list above. An objection can match multiple categories if applicable. OUTPUT FORMAT (JSON only, no additional text): { "classified_objections": [ { "classification_id": , "objection_text": "", "confidence": "" } ] } IMPORTANT: - Return ONLY valid JSON - Only include objections that clearly match a category - If no objections match any category, return empty array - Multiple objections can have the same classification_id classified_objectionszError classifying objections: ) rUrget_connectioncursorexecutefetchallr{warningr+rrErr) rr2rr5connrclassificationsclassifications_textr; ai_responserrrr_classify_objectionsfs<      z!CallAnalyzer._classify_objectionscallidc CszL|||}|duo|tk}|rtd||t||j||d}d|d<t||d} id|d|d |d d d |d d d d ddd||dpRddddddddddt dgd| dd| dd| dd| dd| d| d| dt |d} |j ||| |j |||d|d d | WS|j|} | std!|d"||||||WS|j|| |d} td#|d$|d%t| d&|| }i} i}g}d'}d'}| D]x}|d(}|d)|i}|d*d+rG|d,d'}|durd'}|d-pd'}|j ||}||d+d.| |<d+|d/d |d0d'|d1d2|d3d d4||<||7}||7}q|d-pMd'}d'|d5|d3d6d7| |<||||7}qt||d} |j | \}}|dur}|}|}n|d'krt||d8d9}nd:}|d d }g}|r|rtd;|||||}td} |j ||| |j |||d|d d td?|d@|dA|dB|dC | WStyi}ztdD|d$|dE|d}~ww)Fa Analyze a call with quality parameters and talk-to-listen ratio Args: bid: Business ID callid: Call ID transcript: Full call transcript speaker_segments: List of speaker segments with timing info actual_duration: Actual call duration in seconds Returns: Dict containing all analysis results Nz>Call %s duration %.2fs is below %ss; skipping quality scoring.)r2z%Call duration too low to assign scorequality_score_noter?rr2summaryoverall_summaryrh call_purposeobjections_concernsobjection_typeNone sentimentrL quality_scoreparameter_scoresparameter_detectionstotal_possible_scoreparameters_not_applicabletalk_listen_ratioagent_talk_timecustomer_talk_timedead_air_percentageagent_speak_percentagecustomer_speak_percentage assessmentrtalk_listen_assessmentanalysis_modelrcustomer_profilecustomer_profile_summaryz$No quality parameters found for BID z. Using fallback analysis.zAnalyzing call z for BID z with z parametersrr!r applicableTscorer#)rr#rdetected_in_segment speaker_turn timestampz00:00 reasoning)detectedtranscript_segmentrrrFzNot applicable to this call)rr#rreasondgz Classifying objections for call Found z classified objections)rrrrru ✓ Call z analyzed: Score /z (Total: )zError analyzing call : )rJ MIN_DURATION_FOR_SCORING_SECONDSr{r|rr=rrErYr~rrrsave_call_analyticssave_bant_analysisrget_parametersr_fallback_analysisr<lennormalize_parameter_scorer*+compute_quality_score_from_parameter_scoresroundrUrrr)rr2rr5r>r?duration_seconds is_short_callr ratio_dataanalytics_datarr;rrr total_scorerr- param_name param_resultrr# computed_qs computed_tpsrrrrrrr analyze_callsf          "                         $zCallAnalyzer.analyze_callc Cstd|t||d}|j|dd\}}|rd|dnd} |r%dnd } d |d| d | d } z|| } Wn d dddd} Yid|d|d| ddd| ddd| ddd| ddd|| ddddddddddd dd!|d!d"|d"d#|d#d$|d$d%|d%|d&|d't| d(} |j ||| |j ||| d)| d*d| S)+z;Fallback analysis when no quality parameters are configuredz!Using fallback analysis for call rFr0r)r6rhz0- overall_summary: (follow SUMMARY CONFIG above)z'- overall_summary: 5-6 sentence summaryz7Analyze this call transcript and provide: TRANSCRIPT: zProvide a JSON response with: a - call_purpose: main reason for call - objections_concerns: any issues raised - objection_type: 2-3 word classification (e.g., 'Delivery Issue', 'Food Quality', 'None') - sentiment: positive/negative/neutral - customer_profile: object with budget, authority, need, timeline (value/evidence/reasoning/confidence) - customer_profile_summary: 1-2 sentence summary of BANT status Return ONLY valid JSON.zCall analysis unavailableUnknownrL)rrrrrr2rrrrrrrr2rNrrrrrrrrrrrrr) r{r|rr4rrErYrrrr)rr2rr5r>r?rr8r9r: summary_line basic_promptrrrrrros            zCallAnalyzer._fallback_analysis)N)__name__ __module__ __qualname____doc__rrrrTr/rbooltupler4r<r=rBrJrYrrrrrrrrrs*  $c"2Z(B,Hr r2rlimitc Csdt|}t|}|||}tdt|d|ggd}|D]}z^|||d}|r4|dsAtd|ddWq"|d} |dg} | rht | t rhzt | } Wnt j tfygg} Ynw|d } |||d| | | } |d |dWq"ty} z!td |dd | |d |dt | dWYd} ~ q"d} ~ ww|S)z(Batch analyze calls that need processingrz calls to analyze for BID )successfailedr transcriptszNo transcript found for call z , skippingr>durationrzFailed to analyze call rr)rrN)rrget_calls_for_analysisr{r|rget_raw_call_detailsrErrSrTr~rrrCrr*rr)r2rranalyzerrcallsresultscall call_detailsr5r>r?resultrrrrbatch_analyze_callss<   (r)r) rrr~loggingtypingrrrrosquality_parameters_handlerrtalk_listen_calculatorrrrsummary_configr r getLoggerrr{getenvr r r rrrrTintrrrrrs0         (