update JSON responses

server_executor_cleaned.py

@@ -15,8 +15,8 @@ from aiortc.contrib.media import MediaRelay
 from av import AudioFifo
 from loguru import logger
 from whisper_jax import FlaxWhisperPipline
-
 from utils.run_utils import run_in_executor
+from sortedcontainers import SortedDict
 
 pcs = set()
 relay = MediaRelay()
@@ -34,9 +34,11 @@ last_transcribed_time = 0.0
 LLM_MACHINE_IP = "216.153.52.83"
 LLM_MACHINE_PORT = "5000"
 LLM_URL = f"http://{LLM_MACHINE_IP}:{LLM_MACHINE_PORT}/api/v1/generate"
+incremental_responses = []
+sorted_transcripts = SortedDict()
 
 
-def get_title_and_summary(llm_input_text):
+def get_title_and_summary(llm_input_text, last_timestamp):
     print("Generating title and summary")
     # output = llm.generate(prompt)
 
@@ -47,8 +49,10 @@ def get_title_and_summary(llm_input_text):
 
     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.
+        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}
 
@@ -59,14 +63,22 @@ def get_title_and_summary(llm_input_text):
             "prompt": prompt
     }
 
+    # To-do: 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["timestamp"] =\
+            str(datetime.timedelta(seconds=round(last_timestamp)))
+        incremental_responses.append(output)
+        result = {
+                "cmd": "UPDATE_TOPICS",
+                "topics": incremental_responses,
+        }
     except Exception as e:
-        print(str(e))
-        output = None
-    return output
+        print("Exception" + str(e))
+        result = None
+    return result
 
 
 def channel_log(channel, t, message):
@@ -74,13 +86,32 @@ def channel_log(channel, t, message):
 
 
 def channel_send(channel, message):
-    # channel_log(channel, ">", message)
+    if channel:
+        channel.send(message)
+
+
+def channel_send_increment(channel, message):
     if channel and message:
         channel.send(json.dumps(message))
 
 
+def channel_send_transcript(channel):
+    # channel_log(channel, ">", message)
+    if channel:
+        try:
+            least_time = sorted_transcripts.keys()[0]
+            message = sorted_transcripts[least_time]
+            if message:
+                del sorted_transcripts[least_time]
+                channel.send(json.dumps(message))
+        except Exception as e:
+            print("Exception", str(e))
+            pass
+
+
 def get_transcription(frames):
     print("Transcribing..")
+    sorted_transcripts[frames[0].time] = None
     out_file = io.BytesIO()
     wf = wave.open(out_file, "wb")
     wf.setnchannels(CHANNELS)
@@ -105,10 +136,10 @@ def get_transcription(frames):
     last_transcribed_time += duration
 
     result = {
-            "text": whisper_result["text"],
-            "timestamp": str(datetime.timedelta(seconds=
-                                                round(last_transcribed_time)))
+            "cmd": "SHOW_TRANSCRIPTION",
+            "text": whisper_result["text"]
     }
+    sorted_transcripts[frames[0].time] = result
     return result
 
 
@@ -133,22 +164,24 @@ class AudioStreamTrack(MediaStreamTrack):
                     get_transcription, local_frames, executor=executor
             )
             whisper_result.add_done_callback(
-                    lambda f: channel_send(data_channel, whisper_result.result())
+                    lambda f: channel_send_transcript(data_channel)
                     if f.result()
                     else None
             )
 
-            if len(transcription_text) > 2000:
-                llm_input_text = transcription_text
-                transcription_text = ""
-                llm_result = run_in_executor(get_title_and_summary,
-                                             llm_input_text,
-                                             executor=executor)
-                llm_result.add_done_callback(
-                        lambda f: channel_send(data_channel, llm_result.result())
-                        if f.result()
-                        else None
-                )
+        if len(transcription_text) > 100:
+            llm_input_text = transcription_text
+            transcription_text = ""
+            llm_result = run_in_executor(get_title_and_summary,
+                                         llm_input_text,
+                                         last_transcribed_time,
+                                         executor=executor)
+            llm_result.add_done_callback(
+                    lambda f: channel_send_increment(data_channel,
+                                                     llm_result.result())
+                    if f.result()
+                    else None
+            )
         return frame
 
 
@@ -215,7 +248,9 @@ if __name__ == "__main__":
             app,
             defaults={
                     "*": aiohttp_cors.ResourceOptions(
-                            allow_credentials=True, expose_headers="*", allow_headers="*"
+                            allow_credentials=True,
+                            expose_headers="*",
+                            allow_headers="*"
                     )
             },
     )