server: reformat whole project using black

server/server.py

@@ -17,8 +17,15 @@ from aiortc.contrib.media import MediaRelay
 from faster_whisper import WhisperModel
 
 from reflector_dataclasses import (
-        BlackListedMessages, FinalSummaryResult, ParseLLMResult, TitleSummaryInput,
-        TitleSummaryOutput, TranscriptionInput, TranscriptionOutput, TranscriptionContext)
+    BlackListedMessages,
+    FinalSummaryResult,
+    ParseLLMResult,
+    TitleSummaryInput,
+    TitleSummaryOutput,
+    TranscriptionInput,
+    TranscriptionOutput,
+    TranscriptionContext,
+)
 from utils.log_utils import LOGGER
 from utils.run_utils import CONFIG, run_in_executor, SECRETS
 
@@ -28,9 +35,7 @@ relay = MediaRelay()
 executor = ThreadPoolExecutor()
 
 # Transcription model
-model = WhisperModel("tiny", device="cpu",
-                     compute_type="float32",
-                     num_workers=12)
+model = WhisperModel("tiny", device="cpu", compute_type="float32", num_workers=12)
 
 # Audio configurations
 CHANNELS = int(CONFIG["AUDIO"]["CHANNELS"])
@@ -46,7 +51,10 @@ else:
     LLM_MACHINE_PORT = CONFIG["LLM"]["LLM_MACHINE_PORT"]
     LLM_URL = f"http://{LLM_MACHINE_IP}:{LLM_MACHINE_PORT}/api/v1/generate"
 
-def parse_llm_output(param: TitleSummaryInput, response: requests.Response) -> Union[None, ParseLLMResult]:
+
+def parse_llm_output(
+    param: TitleSummaryInput, response: requests.Response
+) -> Union[None, ParseLLMResult]:
     """
     Function to parse the LLM response
     :param param:
@@ -61,7 +69,9 @@ def parse_llm_output(param: TitleSummaryInput, response: requests.Response) -> U
         return None
 
 
-def get_title_and_summary(ctx: TranscriptionContext, param: TitleSummaryInput) -> Union[None, TitleSummaryOutput]:
+def get_title_and_summary(
+    ctx: TranscriptionContext, param: TitleSummaryInput
+) -> Union[None, TitleSummaryOutput]:
     """
     From the input provided (transcript), query the LLM to generate
     topics and summaries
@@ -72,9 +82,7 @@ def get_title_and_summary(ctx: TranscriptionContext, param: TitleSummaryInput) -
 
     # TODO : Handle unexpected output formats from the model
     try:
-        response = requests.post(LLM_URL,
-                                 headers=param.headers,
-                                 json=param.data)
+        response = requests.post(LLM_URL, headers=param.headers, json=param.data)
         output = parse_llm_output(param, response)
         if output:
             result = output.get_result()
@@ -107,7 +115,9 @@ def channel_send(channel, message: str) -> NoReturn:
         channel.send(message)
 
 
-def channel_send_increment(channel, param: Union[FinalSummaryResult, TitleSummaryOutput]) -> NoReturn:
+def channel_send_increment(
+    channel, param: Union[FinalSummaryResult, TitleSummaryOutput]
+) -> NoReturn:
     """
     Send the incremental topics and summaries via the data channel
     :param channel:
@@ -145,7 +155,9 @@ def channel_send_transcript(ctx: TranscriptionContext) -> NoReturn:
         LOGGER.info("Exception", str(exception))
 
 
-def get_transcription(ctx: TranscriptionContext, input_frames: TranscriptionInput) -> Union[None, TranscriptionOutput]:
+def get_transcription(
+    ctx: TranscriptionContext, input_frames: TranscriptionInput
+) -> Union[None, TranscriptionOutput]:
     """
     From the collected audio frames create transcription by inferring from
     the chosen transcription model
@@ -173,12 +185,13 @@ def get_transcription(ctx: TranscriptionContext, input_frames: TranscriptionInpu
     result_text = ""
 
     try:
-        segments, _ = \
-            model.transcribe(audio_file,
-                             language="en",
-                             beam_size=5,
-                             vad_filter=True,
-                             vad_parameters={"min_silence_duration_ms": 500})
+        segments, _ = model.transcribe(
+            audio_file,
+            language="en",
+            beam_size=5,
+            vad_filter=True,
+            vad_parameters={"min_silence_duration_ms": 500},
+        )
         os.remove(audio_file)
         segments = list(segments)
         result_text = ""
@@ -191,7 +204,7 @@ def get_transcription(ctx: TranscriptionContext, input_frames: TranscriptionInpu
                 start_time = 0.0
             if not segment.end:
                 end_time = 5.5
-            duration += (end_time - start_time)
+            duration += end_time - start_time
 
         ctx.last_transcribed_time += duration
         ctx.transcription_text += result_text
@@ -218,8 +231,9 @@ def get_final_summary_response(ctx: TranscriptionContext) -> FinalSummaryResult:
 
     response = FinalSummaryResult(final_summary, ctx.last_transcribed_time)
 
-    with open("./artefacts/meeting_titles_and_summaries.txt", "a",
-              encoding="utf-8") as file:
+    with open(
+        "./artefacts/meeting_titles_and_summaries.txt", "a", encoding="utf-8"
+    ) as file:
         file.write(json.dumps(ctx.incremental_responses))
 
     return response
@@ -243,33 +257,32 @@ class AudioStreamTrack(MediaStreamTrack):
         frame = await self.track.recv()
         self.audio_buffer.write(frame)
 
-        if local_frames := self.audio_buffer.read_many(AUDIO_BUFFER_SIZE, partial=False):
+        if local_frames := self.audio_buffer.read_many(
+            AUDIO_BUFFER_SIZE, partial=False
+        ):
             whisper_result = run_in_executor(
-                    get_transcription,
-                    ctx,
-                    TranscriptionInput(local_frames),
-                    executor=executor
+                get_transcription,
+                ctx,
+                TranscriptionInput(local_frames),
+                executor=executor,
             )
             whisper_result.add_done_callback(
-                    lambda f: channel_send_transcript(ctx)
-                    if f.result()
-                    else None
+                lambda f: channel_send_transcript(ctx) if f.result() else None
             )
 
         if len(ctx.transcription_text) > 25:
             llm_input_text = ctx.transcription_text
             ctx.transcription_text = ""
-            param = TitleSummaryInput(input_text=llm_input_text,
-                                      transcribed_time=ctx.last_transcribed_time)
-            llm_result = run_in_executor(get_title_and_summary,
-                                         ctx,
-                                         param,
-                                         executor=executor)
+            param = TitleSummaryInput(
+                input_text=llm_input_text, transcribed_time=ctx.last_transcribed_time
+            )
+            llm_result = run_in_executor(
+                get_title_and_summary, ctx, param, executor=executor
+            )
             llm_result.add_done_callback(
-                    lambda f: channel_send_increment(ctx.data_channel,
-                                                     llm_result.result())
-                    if f.result()
-                    else None
+                lambda f: channel_send_increment(ctx.data_channel, llm_result.result())
+                if f.result()
+                else None
             )
         return frame
 
@@ -328,13 +341,10 @@ async def offer(request: requests.Request) -> web.Response:
     answer = await pc.createAnswer()
     await pc.setLocalDescription(answer)
     return web.Response(
-            content_type="application/json",
-            text=json.dumps(
-                    {
-                            "sdp": pc.localDescription.sdp,
-                            "type": pc.localDescription.type
-                    }
-            ),
+        content_type="application/json",
+        text=json.dumps(
+            {"sdp": pc.localDescription.sdp, "type": pc.localDescription.type}
+        ),
     )
 
 
@@ -351,26 +361,22 @@ async def on_shutdown(application: web.Application) -> NoReturn:
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-            description="WebRTC based server for Reflector"
+    parser = argparse.ArgumentParser(description="WebRTC based server for Reflector")
+    parser.add_argument(
+        "--host", default="0.0.0.0", help="Server host IP (def: 0.0.0.0)"
     )
     parser.add_argument(
-            "--host", default="0.0.0.0", help="Server host IP (def: 0.0.0.0)"
-    )
-    parser.add_argument(
-            "--port", type=int, default=1250, help="Server port (def: 1250)"
+        "--port", type=int, default=1250, help="Server port (def: 1250)"
     )
     args = parser.parse_args()
     app = web.Application()
     cors = aiohttp_cors.setup(
-            app,
-            defaults={
-                    "*": aiohttp_cors.ResourceOptions(
-                            allow_credentials=True,
-                            expose_headers="*",
-                            allow_headers="*"
-                    )
-            },
+        app,
+        defaults={
+            "*": aiohttp_cors.ResourceOptions(
+                allow_credentials=True, expose_headers="*", allow_headers="*"
+            )
+        },
     )
 
     offer_resource = cors.add(app.router.add_resource("/offer"))