Moved all server files to server/

server/reflector_dataclasses.py

@@ -0,0 +1,164 @@
+"""
+Collection of data classes for streamlining and rigidly structuring
+the input and output parameters of functions
+"""
+
+import datetime
+from dataclasses import dataclass
+from typing import List
+
+import av
+
+
+@dataclass
+class TitleSummaryInput:
+    """
+    Data class for the input to generate title and summaries.
+    The outcome will be used to send query to the LLM for processing.
+    """
+    input_text = str
+    transcribed_time = float
+    prompt = str
+    data = dict
+
+    def __init__(self, transcribed_time, input_text=""):
+        self.input_text = input_text
+        self.transcribed_time = transcribed_time
+        self.prompt = \
+            f"""
+            ### Human:
+            Create a JSON object as response.The JSON object must have 2 fields:
+            i) title and ii) summary.For the title field,generate a short title
+            for the given text. For the summary field, summarize the given text
+            in three sentences.
+
+            {self.input_text}
+
+            ### Assistant:
+            """
+        self.data = {"data": self.prompt}
+        self.headers = {"Content-Type": "application/json"}
+
+
+@dataclass
+class IncrementalResult:
+    """
+    Data class for the result of generating one title and summaries.
+    Defines how a single "topic" looks like.
+    """
+    title = str
+    description = str
+    transcript = str
+
+    def __init__(self, title, desc, transcript):
+        self.title = title
+        self.description = desc
+        self.transcript = transcript
+
+
+@dataclass
+class TitleSummaryOutput:
+    """
+    Data class for the result of all generated titles and summaries.
+    The result will be sent back to the client
+    """
+    cmd = str
+    topics = List[IncrementalResult]
+
+    def __init__(self, inc_responses):
+        self.topics = inc_responses
+
+    def get_result(self):
+        return {
+                "cmd": self.cmd,
+                "topics": self.topics
+        }
+
+
+@dataclass
+class ParseLLMResult:
+    """
+    Data class to parse the result returned by the LLM while generating title
+    and summaries. The result will be sent back to the client.
+    """
+    description = str
+    transcript = str
+    timestamp = str
+
+    def __init__(self, param: TitleSummaryInput, output: dict):
+        self.transcript = param.input_text
+        self.description = output.pop("summary")
+        self.timestamp = \
+            str(datetime.timedelta(seconds=round(param.transcribed_time)))
+
+    def get_result(self):
+        return {
+                "description": self.description,
+                "transcript": self.transcript,
+                "timestamp": self.timestamp
+        }
+
+
+@dataclass
+class TranscriptionInput:
+    """
+    Data class to define the input to the transcription function
+    AudioFrames -> input
+    """
+    frames = List[av.audio.frame.AudioFrame]
+
+    def __init__(self, frames):
+        self.frames = frames
+
+
+@dataclass
+class TranscriptionOutput:
+    """
+    Dataclass to define the result of the transcription function.
+    The result will be sent back to the client
+    """
+    cmd = str
+    result_text = str
+
+    def __init__(self, result_text):
+        self.cmd = "SHOW_TRANSCRIPTION"
+        self.result_text = result_text
+
+    def get_result(self):
+        return {
+                "cmd": self.cmd,
+                "text": self.result_text
+        }
+
+
+@dataclass
+class FinalSummaryResult:
+    """
+    Dataclass to define the result of the final summary function.
+    The result will be sent back to the client.
+    """
+    cmd = str
+    final_summary = str
+    duration = str
+
+    def __init__(self, final_summary, time):
+        self.duration = str(datetime.timedelta(seconds=round(time)))
+        self.final_summary = final_summary
+        self.cmd = ""
+
+    def get_result(self):
+        return {
+                "cmd": self.cmd,
+                "duration": self.duration,
+                "summary": self.final_summary
+        }
+
+
+class BlackListedMessages:
+    """
+    Class to hold the blacklisted messages. These messages should be filtered
+    out and not sent back to the client as part of the transcription.
+    """
+    messages = [" Thank you.", " See you next time!",
+                " Thank you for watching!", " Bye!",
+                " And that's what I'm talking about."]