Ë ™hÚãó˜—ddlmZmZddlmZddlmZmZmZm Z m Z ddl m Z m Z mZddlZGd„de«ZGd„d e«ZGd „d «Zy) é)ÚListÚDict)ÚField)Ú AtomicAgentÚ AgentConfigÚ BaseIOSchemaÚBaseToolÚBaseToolConfig)Ú ChatHistoryÚSystemPromptGeneratorÚBaseDynamicContextProviderNcó6—eZdZUdZedd¬«Zeeed<y)ÚReviewSentimentInputSchemazJ Schema for a batch of user reviews to be analyzed for sentiment. .z List of user reviews to analyze.©Ú descriptionÚreviewsN) Ú__name__Ú __module__Ú __qualname__Ú__doc__rrrÚstrÚ__annotations__©óú:/var/www/html/hubwallet-dev/src/core/sentiment_analyzer.pyrr s…ññ˜sÐ0RÔS€GˆT#‰YÔSrrcó—eZdZUdZedd¬«Zeed<edd¬«Ze eed<edd¬«Z eed <edd ¬«Z e eefed <edd ¬«Z e eefed <y)ÚReviewSentimentOutputSchemaz= Schema for analyzed sentiment data from the review. .z Overall sentiment of the review.rÚoverall_sentimentzList of relevant emotions.Úrelevant_emotionsz)Overall arousal level: active or passive.Úarousalz"Sentiment for each relevant topic.Úrelevant_topics_sentimentz8Sentiment for important words extracted from the review.Úimportant_words_sentimentN)rrrrrrrrrrr r!rr"rrrrrs‡…ññ# 3Ð4VÔWÐsÓWÙ#(¨Ð:VÔ#WÐt˜C‘yÓWÙ˜Ð*UÔV€GˆSÓVÙ05Ø Ð=ô1Иt C¨ H™~óñ16Ø ÐSô1Иt C¨ H™~ôrrcóJ—eZdZd dedefd„Zdeedeefd„Zdedefd„Z y ) ÚReviewSentimentAnalyzerÚmodelÚ temperaturecó‚—tdggd¢dg¬«}t||||¬«}tttf|¬«|_y)a  Initialize the ReviewSentimentAnalyzer for batch processing. Args: client: The LLM client (e.g., from instructor.from_openai) model: The model identifier to use temperature: Sampling temperature for the LLM zTYou are an expert sentiment analysis system that processes multiple reviews at once.) z7Analyze each review in the provided list independently.zFor each review, determine:z91. Overall sentiment (positive, negative, mixed, neutral)zG2. Relevant emotions with arousal levels (e.g., [{'anger': 'passive'}])z>3. Sentiment for relevant topics (Service, Food Quality, etc.)z5Evaluate sentiment for each of the following topics: zSService Experience, Cleanliness & Hygiene, Ambiance & Atmosphere, Pricing & Value, zdOnline Ordering & Delivery, Complaint Resolution, Customer Loyalty, Menu Preferences, Waiting Time, ziPortion Size, Staff Friendliness, Payment Experience, Special Requests Handling, Reservation Experience, z^Parking & Accessibility, Food Presentation, Seasonal Trends, Offer & Promotions, Food Quality.z.4. Sentiment for important words in the reviewzMMaintain the exact same order of reviews in your output as received in input.z3Return all results in a single structured response.a>Return a JSON object with a single key 'results' that contains a list of dicts. Each dict includes: - overall_sentiment - relevant_emotions - arousal - relevant_topics_sentiment - important_words_sentiment )Ú backgroundÚstepsÚoutput_instructions)Úclientr%r&Úsystem_prompt_generator)ÚconfigN)r rrrrÚagent)Úselfr+r%r&r,r-s rÚ__init__z ReviewSentimentAnalyzer.__init__"sW€ô#8ØnÐoòð ð !ô## Ðô:ØØØ#Ø$;ô  ˆô!Ô!;ÔrAsCðßÝ÷[ÕZß`Ñ`ã ôT ôTô  ,ô ÷F-òF-r