add data classes and typing

reflector_dataclasses.py

@@ -0,0 +1,119 @@
+import datetime
+from dataclasses import dataclass
+from typing import List
+
+import av
+
+
+@dataclass
+class TitleSummaryInput:
+    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 IncrementalResponse:
+    title = str
+    description = str
+    transcript = str
+
+    def __init__(self, title, desc, transcript):
+        self.title = title
+        self.description = desc
+        self.transcript = transcript
+
+
+@dataclass
+class TitleSummaryOutput:
+    cmd = str
+    topics = List[IncrementalResponse]
+
+    def __init__(self, inc_responses):
+        self.topics = inc_responses
+
+    def get_response(self):
+        return {
+                "cmd": self.cmd,
+                "topics": self.topics
+        }
+
+
+@dataclass
+class ParseLLMResult:
+    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:
+    frames = List[av.audio.frame.AudioFrame]
+
+    def __init__(self, frames):
+        self.frames = frames
+
+
+@dataclass
+class TranscriptionOutput:
+    cmd = str
+    result_text = str
+
+    def __init__(self, result_text):
+        self.cmd = "SHOW_TRANSCRIPTION"
+        self.result_text = result_text
+
+    def get_response(self):
+        return {
+                "cmd": self.cmd,
+                "text": self.result_text
+        }
+
+
+@dataclass
+class FinalSummaryResponse:
+    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_response(self):
+        return {
+                "cmd": self.cmd,
+                "duration": self.duration,
+                "summary": self.final_summary
+        }

server.py

@@ -6,6 +6,7 @@ import os
 import uuid
 import wave
 from concurrent.futures import ThreadPoolExecutor
+from typing import Any
 
 import aiohttp_cors
 import requests
@@ -17,7 +18,9 @@ from faster_whisper import WhisperModel
 from loguru import logger
 from sortedcontainers import SortedDict
 
-from utils.run_utils import run_in_executor, config
+from reflector_dataclasses import FinalSummaryResponse, ParseLLMResult, TitleSummaryInput, TitleSummaryOutput, \
+    TranscriptionInput, TranscriptionOutput
+from utils.run_utils import config, run_in_executor
 
 pcs = set()
 relay = MediaRelay()
@@ -43,49 +46,30 @@ blacklisted_messages = [" Thank you.", " See you next time!",
                         " And that's what I'm talking about."]
 
 
-def get_title_and_summary(llm_input_text, last_timestamp):
+def parse_llm_output(param: TitleSummaryInput, response: requests.Response) -> Any[None, ParseLLMResult]:
+    try:
+        output = json.loads(response.json()["results"][0]["text"])
+        return ParseLLMResult(param, output).get_result()
+    except Exception as e:
+        logger.info("Exception" + str(e))
+        return None
+
+
+def get_title_and_summary(param: TitleSummaryInput) -> Any[None, TitleSummaryOutput]:
     logger.info("Generating title and summary")
-    # output = llm.generate(prompt)
-
-    # Use monadical-ml to fire this query to an LLM and get result
-    headers = {
-            "Content-Type": "application/json"
-    }
-
-    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.
-
-        {llm_input_text}
-
-        ### Assistant:
-        """
-
-    data = {
-            "prompt": prompt
-    }
 
     # TODO : Handle unexpected output formats from the model
     try:
-        response = requests.post(LLM_URL, headers=headers, json=data)
-        output = json.loads(response.json()["results"][0]["text"])
-        output["description"] = output.pop("summary")
-        output["transcript"] = llm_input_text
-        output["timestamp"] = \
-            str(datetime.timedelta(seconds=round(last_timestamp)))
-        incremental_responses.append(output)
-        result = {
-                "cmd": "UPDATE_TOPICS",
-                "topics": incremental_responses,
-        }
-
+        response = requests.post(LLM_URL,
+                                 headers=param.headers,
+                                 json=param.data)
+        output = parse_llm_output(param, response)
+        if output:
+            incremental_responses.append(output)
+            return TitleSummaryOutput(incremental_responses).get_response()
     except Exception as e:
         logger.info("Exception" + str(e))
-        result = None
-    return result
+        return None
 
 
 def channel_log(channel, t, message):
@@ -123,11 +107,11 @@ def channel_send_transcript(channel):
             pass
 
 
-def get_transcription(frames):
+def get_transcription(input_frames: TranscriptionInput) -> Any[None, TranscriptionOutput]:
     logger.info("Transcribing..")
-    sorted_transcripts[frames[0].time] = None
+    sorted_transcripts[input_frames[0].time] = None
 
-    # TODO:
+    # TODO: Find cleaner way, watch "no transcription" issue below
     # Passing IO objects instead of temporary files throws an error
     # Passing ndarrays (typecasted with float) does not give any
     # transcription. Refer issue,
@@ -138,7 +122,7 @@ def get_transcription(frames):
     wf.setframerate(RATE)
     wf.setsampwidth(2)
 
-    for frame in frames:
+    for frame in input_frames.frames:
         wf.writeframes(b"".join(frame.to_ndarray()))
     wf.close()
 
@@ -173,30 +157,23 @@ def get_transcription(frames):
         logger.info("Exception" + str(e))
         pass
 
-    result = {
-            "cmd": "SHOW_TRANSCRIPTION",
-            "text": result_text
-    }
-    sorted_transcripts[frames[0].time] = result
+    result = TranscriptionOutput(result_text).get_response()
+    sorted_transcripts[input_frames.frames[0].time] = result
     return result
 
 
-def get_final_summary_response():
+def get_final_summary_response() -> Any[None, FinalSummaryResponse]:
     final_summary = ""
 
     # Collate inc summaries
     for topic in incremental_responses:
         final_summary += topic["description"]
 
-    response = {
-            "cmd": "DISPLAY_FINAL_SUMMARY",
-            "duration": str(datetime.timedelta(
-                    seconds=round(last_transcribed_time))),
-            "summary": final_summary
-    }
+    response = FinalSummaryResponse(final_summary, last_transcribed_time).get_response()
 
     with open("./artefacts/meeting_titles_and_summaries.txt", "a") as f:
         f.write(json.dumps(incremental_responses))
+
     return response
 
 
@@ -218,7 +195,9 @@ class AudioStreamTrack(MediaStreamTrack):
 
         if local_frames := audio_buffer.read_many(256 * 960, partial=False):
             whisper_result = run_in_executor(
-                    get_transcription, local_frames, executor=executor
+                    get_transcription,
+                    TranscriptionInput(local_frames),
+                    executor=executor
             )
             whisper_result.add_done_callback(
                     lambda f: channel_send_transcript(data_channel)
@@ -226,12 +205,13 @@ class AudioStreamTrack(MediaStreamTrack):
                     else None
             )
 
-        if len(transcription_text) > 750:
+        if len(transcription_text) > 25:
             llm_input_text = transcription_text
             transcription_text = ""
+            param = TitleSummaryInput(input_text=llm_input_text,
+                                      transcribed_time=last_transcribed_time)
             llm_result = run_in_executor(get_title_and_summary,
-                                         llm_input_text,
-                                         last_transcribed_time,
+                                         param,
                                          executor=executor)
             llm_result.add_done_callback(
                     lambda f: channel_send_increment(data_channel,
@@ -332,4 +312,4 @@ if __name__ == "__main__":
     offer_resource = cors.add(app.router.add_resource("/offer"))
     cors.add(offer_resource.add_route("POST", offer))
     app.on_shutdown.append(on_shutdown)
-    web.run_app(app, access_log=None,  host=args.host, port=args.port)
+    web.run_app(app, access_log=None, host=args.host, port=args.port)