Update app.py

app.py

@@ -1,11 +1,9 @@
 import spaces
 import torch
-
 import gradio as gr
 import yt_dlp as youtube_dl
-from transformers import pipeline
-from transformers.pipelines.audio_utils import ffmpeg_read
-
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
+from threading import Thread
 import tempfile
 import os
 
@@ -16,6 +14,7 @@ YT_LENGTH_LIMIT_S = 3600  # limit to 1 hour YouTube files
 
 device = 0 if torch.cuda.is_available() else "cpu"
 
+# Initialize the transcription pipeline
 pipe = pipeline(
     task="automatic-speech-recognition",
     model=MODEL_NAME,
@@ -23,125 +22,104 @@ pipe = pipeline(
     device=device,
 )
 
-
+# Hugging Face Token for the LLM model
+HF_TOKEN = os.getenv("HF_TOKEN")  # Make sure to set this in the environment variables
+
+# Load tokenizer and model for SOAP note generation
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
+model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct", device_map="auto")
+
+# Prompt for SOAP note generation
+sys_prompt = "You are a world class clinical assistant."
+task_prompt = """
+Convert the following transcribed conversation into a clinical SOAP note.
+The text includes dialogue between a physician and a patient. Please clearly distinguish between the physician's and the patient's statements.
+Extract and organize the information into the relevant sections of a SOAP note:
+- Subjective (symptoms and patient statements),
+- Objective (clinical findings and observations, these might be missing if the physician has not conducted a physical exam or has not verbally stated findings),
+- Assessment (diagnosis or potential diagnoses, objectively provide a top 5 most likely diagnosis based on just the subjective findings, and use the objective findings if available),
+- Plan (treatment and follow-up).
+Ensure the note is concise, clear, and accurately reflects the conversation.
+"""
+
+# Function to transcribe audio inputs
 @spaces.GPU
 def transcribe(inputs, task):
     if inputs is None:
         raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
-
     text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
-    return  text
-
-
-def _return_yt_html_embed(yt_url):
-    video_id = yt_url.split("?v=")[-1]
-    HTML_str = (
-        f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
-        " </center>"
-    )
-    return HTML_str
+    return text
 
+# Function to download audio from YouTube
 def download_yt_audio(yt_url, filename):
     info_loader = youtube_dl.YoutubeDL()
-    
     try:
         info = info_loader.extract_info(yt_url, download=False)
     except youtube_dl.utils.DownloadError as err:
         raise gr.Error(str(err))
-    
-    file_length = info["duration_string"]
-    file_h_m_s = file_length.split(":")
-    file_h_m_s = [int(sub_length) for sub_length in file_h_m_s]
-    
-    if len(file_h_m_s) == 1:
-        file_h_m_s.insert(0, 0)
-    if len(file_h_m_s) == 2:
-        file_h_m_s.insert(0, 0)
-    file_length_s = file_h_m_s[0] * 3600 + file_h_m_s[1] * 60 + file_h_m_s[2]
-    
+
+    file_length_s = sum(x * int(t) for x, t in zip([3600, 60, 1], info["duration_string"].split(":")) if t.isdigit())
     if file_length_s > YT_LENGTH_LIMIT_S:
-        yt_length_limit_hms = time.strftime("%HH:%MM:%SS", time.gmtime(YT_LENGTH_LIMIT_S))
-        file_length_hms = time.strftime("%HH:%MM:%SS", time.gmtime(file_length_s))
-        raise gr.Error(f"Maximum YouTube length is {yt_length_limit_hms}, got {file_length_hms} YouTube video.")
-    
+        raise gr.Error(f"Video too long. Maximum allowed duration is {YT_LENGTH_LIMIT_S / 60} minutes.")
+
     ydl_opts = {"outtmpl": filename, "format": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best"}
-    
     with youtube_dl.YoutubeDL(ydl_opts) as ydl:
-        try:
-            ydl.download([yt_url])
-        except youtube_dl.utils.ExtractorError as err:
-            raise gr.Error(str(err))
+        ydl.download([yt_url])
 
+# Function to transcribe YouTube audio
 @spaces.GPU
-def yt_transcribe(yt_url, task, max_filesize=75.0):
-    html_embed_str = _return_yt_html_embed(yt_url)
-
+def yt_transcribe(yt_url, task):
     with tempfile.TemporaryDirectory() as tmpdirname:
         filepath = os.path.join(tmpdirname, "video.mp4")
         download_yt_audio(yt_url, filepath)
         with open(filepath, "rb") as f:
             inputs = f.read()
-
-    inputs = ffmpeg_read(inputs, pipe.feature_extractor.sampling_rate)
+    inputs = pipe.feature_extractor.ffmpeg_read(inputs, pipe.feature_extractor.sampling_rate)
     inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}
-
     text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
+    return f'<iframe width="500" height="320" src="https://www.youtube.com/embed/{yt_url.split("?v=")[-1]}"> </iframe>', text
 
-    return html_embed_str, text
-
+# Function to generate SOAP notes using LLM
+def generate_soap(transcribed_text):
+    prompt = f"{sys_prompt}\n\n{task_prompt}\n{transcribed_text}"
+    inputs = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)
+    outputs = model.generate(inputs, max_new_tokens=512)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
 
+# Gradio Interfaces for different inputs
 demo = gr.Blocks(theme=gr.themes.Ocean())
 
 mf_transcribe = gr.Interface(
     fn=transcribe,
-    inputs=[
-        gr.Audio(sources="microphone", type="filepath"),
-        gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
-    ],
+    inputs=[gr.Audio(sources="microphone", type="filepath"), gr.Radio(["transcribe", "translate"], label="Task", value="transcribe")],
     outputs="text",
     title="Whisper Large V3 Turbo: Transcribe Audio",
-    description=(
-        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the"
-        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
-        " of arbitrary length."
-    ),
-    allow_flagging="never",
+    description="Transcribe long-form microphone or audio inputs."
 )
 
 file_transcribe = gr.Interface(
     fn=transcribe,
-    inputs=[
-        gr.Audio(sources="upload", type="filepath", label="Audio file"),
-        gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
-    ],
+    inputs=[gr.Audio(sources="upload", type="filepath", label="Audio file"), gr.Radio(["transcribe", "translate"], label="Task", value="transcribe")],
     outputs="text",
-    title="Whisper Large V3: Transcribe Audio",
-    description=(
-        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the"
-        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
-        " of arbitrary length."
-    ),
-    allow_flagging="never",
+    title="Whisper Large V3: Transcribe Audio"
 )
 
 yt_transcribe = gr.Interface(
     fn=yt_transcribe,
-    inputs=[
-        gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
-        gr.Radio(["transcribe", "translate"], label="Task", value="transcribe")
-    ],
+    inputs=[gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"), gr.Radio(["transcribe", "translate"], label="Task", value="transcribe")],
     outputs=["html", "text"],
-    title="Whisper Large V3: Transcribe YouTube",
-    description=(
-        "Transcribe long-form YouTube videos with the click of a button! Demo uses the checkpoint"
-        f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe video files of"
-        " arbitrary length."
-    ),
-    allow_flagging="never",
+    title="Whisper Large V3: Transcribe YouTube"
+)
+
+soap_note = gr.Interface(
+    fn=generate_soap,
+    inputs="text",
+    outputs="text",
+    title="Generate Clinical SOAP Note",
+    description="Convert transcribed conversation to a clinical SOAP note with structured sections (Subjective, Objective, Assessment, Plan)."
 )
 
 with demo:
-    gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe], ["Microphone", "Audio file", "YouTube"])
+    gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe, soap_note], ["Microphone", "Audio file", "YouTube", "SOAP Note"])
 
 demo.queue().launch(ssr_mode=False)
-