Update app.py

app.py

@@ -61,14 +61,13 @@ def transcribe_audio(audio_path):
         # Read the audio file and prepare inputs for Whisper
         inputs = ffmpeg_read(audio_path, whisper_pipeline.feature_extractor.sampling_rate)
         inputs = {"array": inputs, "sampling_rate": whisper_pipeline.feature_extractor.sampling_rate}
-        
+
         # Perform transcription using Whisper
         result = whisper_pipeline(inputs, batch_size=BATCH_SIZE, return_timestamps=False)
         return result["text"]
     except Exception as e:
         return f"Error during transcription: {e}"
 
-
 # Classify the sentence to the correct SOAP section
 def classify_sentence(sentence):
     similarities = {section: util.pytorch_cos_sim(embedder.encode(sentence), soap_embeddings[section]) for section in soap_prompts.keys()}
@@ -134,52 +133,17 @@ def process_file(file, user_prompt):
     soap_note = soap_analysis(transcription)
     print("SOAP Notes: ", soap_note)
 
-    # # Generate template and JSON using LLaMA
-    # template_output = llama_query(user_prompt, soap_note)
-    # print("Template: ", template_output)
-
-    # json_output = llama_convert_to_json(template_output)
-
     # Clean up temporary files
     if file.name.endswith(".mp4"):
         os.remove(temp_mp3_path)
 
-    return soap_note#, template_output, json_output
+    return soap_note
 
 # Process text function for text input to SOAP
 def process_text(text, user_prompt):
     soap_note = soap_analysis(text)
     print(soap_note)
-
-    # template_output = llama_query(user_prompt, soap_note)
-    # print(template_output)
-    # json_output = llama_convert_to_json(template_output)
-
-    return soap_note#, template_output, json_output
-
-# # Llama query function
-# def llama_query(user_prompt, soap_note, model="llama3.2"):
-#     combined_prompt = f"User Instructions:\n{user_prompt}\n\nContext:\n{soap_note}"
-#     try:
-#         process = Popen(['ollama', 'run', model], stdin=PIPE, stdout=PIPE, stderr=PIPE, text=True, encoding='utf-8')
-#         stdout, stderr = process.communicate(input=combined_prompt)
-#         if process.returncode != 0:
-#             return f"Error: {stderr.strip()}"
-#         return stdout.strip()
-#     except Exception as e:
-#         return f"Unexpected error: {str(e)}"
-
-# # Convert the response to JSON format
-# def llama_convert_to_json(template_output, model="llama3.2"):
-#     json_prompt = f"Convert the following template into a structured JSON format:\n\n{template_output}"
-#     try:
-#         process = Popen(['ollama', 'run', model], stdin=PIPE, stdout=PIPE, stderr=PIPE, text=True, encoding='utf-8')
-#         stdout, stderr = process.communicate(input=json_prompt)
-#         if process.returncode != 0:
-#             return f"Error: {stderr.strip()}"
-#         return stdout.strip()  # Assuming the model outputs a valid JSON string
-#     except Exception as e:
-#         return f"Unexpected error: {str(e)}"
+    return soap_note
 
 # Gradio interface
 def launch_gradio():
@@ -192,8 +156,6 @@ def launch_gradio():
                 inputs=[gr.File(label="Upload Audio/Video File"), gr.Textbox(label="Enter Prompt for Template", placeholder="Enter a detailed prompt...", lines=6)],
                 outputs=[
                     gr.Textbox(label="SOAP Note"),
-                    # gr.Textbox(label="Generated Template from LLaMA"),
-                    # gr.Textbox(label="JSON Output")
                 ],
             )
 
@@ -203,8 +165,6 @@ def launch_gradio():
                 inputs=[gr.Textbox(label="Enter Text", placeholder="Enter medical notes...", lines=6), gr.Textbox(label="Enter Prompt for Template", placeholder="Enter a detailed prompt...", lines=6)],
                 outputs=[
                     gr.Textbox(label="SOAP Note"),
-                    # gr.Textbox(label="Generated Template from LLaMA"),
-                    # gr.Textbox(label="JSON Output")
                 ],
             )
 
@@ -213,3 +173,227 @@ def launch_gradio():
 # Run the Gradio app
 if __name__ == "__main__":
     launch_gradio()
+
+
+
+
+
+
+
+
+
+# import os
+# import tempfile
+# from subprocess import Popen, PIPE
+# import torch
+# import gradio as gr
+# from pydub import AudioSegment
+# from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
+# from transformers.pipelines.audio_utils import ffmpeg_read
+# from sentence_transformers import SentenceTransformer, util
+# import spacy
+# import spacy.cli
+# spacy.cli.download("en_core_web_sm")
+
+# # Constants
+# MODEL_NAME = "openai/whisper-large-v3-turbo"
+# BATCH_SIZE = 8
+# FILE_LIMIT_MB = 1000
+# device = 0 if torch.cuda.is_available() else "cpu"
+
+# # Whisper pipeline
+# whisper_pipeline = pipeline(
+#     task="automatic-speech-recognition",
+#     model=MODEL_NAME,
+#     chunk_length_s=30,
+#     device=device,
+# )
+
+# # NLP model and other helpers
+# nlp = spacy.load("en_core_web_sm")
+# embedder = SentenceTransformer("all-MiniLM-L6-v2")
+
+# # Summarization model
+# summarizer_model_name = "Mahalingam/DistilBart-Med-Summary"
+# tokenizer = AutoTokenizer.from_pretrained(summarizer_model_name)
+# summarizer_model = AutoModelForSeq2SeqLM.from_pretrained(summarizer_model_name)
+# summarizer = pipeline("summarization", model=summarizer_model, tokenizer=tokenizer)
+
+# # SOAP prompts and embeddings
+# soap_prompts = {
+#     "subjective": "Personal reports, symptoms described by patients, or personal health concerns. Details reflecting individual symptoms or health descriptions.",
+#     "objective": "Observable facts, clinical findings, professional observations, specific medical specialties, and diagnoses.",
+#     "assessment": "Clinical assessments, expertise-based opinions on conditions, and significance of medical interventions. Focused on medical evaluations or patient condition summaries.",
+#     "plan": "Future steps, recommendations for treatment, follow-up instructions, and healthcare management plans."
+# }
+# soap_embeddings = {section: embedder.encode(prompt, convert_to_tensor=True) for section, prompt in soap_prompts.items()}
+
+# # Convert MP4 to MP3
+# def convert_mp4_to_mp3(mp4_path, mp3_path):
+#     try:
+#         audio = AudioSegment.from_file(mp4_path, format="mp4")
+#         audio.export(mp3_path, format="mp3")
+#     except Exception as e:
+#         raise RuntimeError(f"Error converting MP4 to MP3: {e}")
+
+# # Transcribe audio
+# def transcribe_audio(audio_path):
+#     try:
+#         if not os.path.exists(audio_path):
+#             raise FileNotFoundError(f"Audio file not found: {audio_path}")
+
+#         # Read the audio file and prepare inputs for Whisper
+#         inputs = ffmpeg_read(audio_path, whisper_pipeline.feature_extractor.sampling_rate)
+#         inputs = {"array": inputs, "sampling_rate": whisper_pipeline.feature_extractor.sampling_rate}
+        
+#         # Perform transcription using Whisper
+#         result = whisper_pipeline(inputs, batch_size=BATCH_SIZE, return_timestamps=False)
+#         return result["text"]
+#     except Exception as e:
+#         return f"Error during transcription: {e}"
+
+
+# # Classify the sentence to the correct SOAP section
+# def classify_sentence(sentence):
+#     similarities = {section: util.pytorch_cos_sim(embedder.encode(sentence), soap_embeddings[section]) for section in soap_prompts.keys()}
+#     return max(similarities, key=similarities.get)
+
+# # Summarize the section if it's too long
+# def summarize_section(section_text):
+#     if len(section_text.split()) < 50:
+#         return section_text
+#     target_length = int(len(section_text.split()) * 0.50)
+#     inputs = tokenizer.encode(section_text, return_tensors="pt", truncation=True, max_length=1024)
+#     summary_ids = summarizer_model.generate(
+#         inputs,
+#         max_length=target_length,
+#         min_length=int(target_length * 0.45),
+#         length_penalty=1.0,
+#         num_beams=4
+#     )
+#     return tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+# # Analyze the SOAP content and divide into sections
+# def soap_analysis(text):
+#     doc = nlp(text)
+#     soap_note = {section: "" for section in soap_prompts.keys()}
+
+#     for sentence in doc.sents:
+#         section = classify_sentence(sentence.text)
+#         soap_note[section] += sentence.text + " "
+
+#     # Summarize each section of the SOAP note
+#     for section in soap_note:
+#         soap_note[section] = summarize_section(soap_note[section].strip())
+
+#     return format_soap_output(soap_note)
+
+# # Format the SOAP note output
+# def format_soap_output(soap_note):
+#     return (
+#         f"Subjective:\n{soap_note['subjective']}\n\n"
+#         f"Objective:\n{soap_note['objective']}\n\n"
+#         f"Assessment:\n{soap_note['assessment']}\n\n"
+#         f"Plan:\n{soap_note['plan']}\n"
+#     )
+
+# # Process file function for audio/video to SOAP
+# def process_file(file, user_prompt):
+#     # Determine file type and convert if necessary
+#     if file.name.endswith(".mp4"):
+#         temp_mp3_path = tempfile.NamedTemporaryFile(suffix=".mp3", delete=False).name
+#         try:
+#             convert_mp4_to_mp3(file.name, temp_mp3_path)
+#             audio_path = temp_mp3_path
+#         except Exception as e:
+#             return f"Error during MP4 to MP3 conversion: {e}", "", ""
+#     else:
+#         audio_path = file.name
+
+#     # Transcribe audio
+#     transcription = transcribe_audio(audio_path)
+#     print("Transcribed Text: ", transcription)
+
+#     # Perform SOAP analysis
+#     soap_note = soap_analysis(transcription)
+#     print("SOAP Notes: ", soap_note)
+
+#     # # Generate template and JSON using LLaMA
+#     # template_output = llama_query(user_prompt, soap_note)
+#     # print("Template: ", template_output)
+
+#     # json_output = llama_convert_to_json(template_output)
+
+#     # Clean up temporary files
+#     if file.name.endswith(".mp4"):
+#         os.remove(temp_mp3_path)
+
+#     return soap_note#, template_output, json_output
+
+# # Process text function for text input to SOAP
+# def process_text(text, user_prompt):
+#     soap_note = soap_analysis(text)
+#     print(soap_note)
+
+#     # template_output = llama_query(user_prompt, soap_note)
+#     # print(template_output)
+#     # json_output = llama_convert_to_json(template_output)
+
+#     return soap_note#, template_output, json_output
+
+# # # Llama query function
+# # def llama_query(user_prompt, soap_note, model="llama3.2"):
+# #     combined_prompt = f"User Instructions:\n{user_prompt}\n\nContext:\n{soap_note}"
+# #     try:
+# #         process = Popen(['ollama', 'run', model], stdin=PIPE, stdout=PIPE, stderr=PIPE, text=True, encoding='utf-8')
+# #         stdout, stderr = process.communicate(input=combined_prompt)
+# #         if process.returncode != 0:
+# #             return f"Error: {stderr.strip()}"
+# #         return stdout.strip()
+# #     except Exception as e:
+# #         return f"Unexpected error: {str(e)}"
+
+# # # Convert the response to JSON format
+# # def llama_convert_to_json(template_output, model="llama3.2"):
+# #     json_prompt = f"Convert the following template into a structured JSON format:\n\n{template_output}"
+# #     try:
+# #         process = Popen(['ollama', 'run', model], stdin=PIPE, stdout=PIPE, stderr=PIPE, text=True, encoding='utf-8')
+# #         stdout, stderr = process.communicate(input=json_prompt)
+# #         if process.returncode != 0:
+# #             return f"Error: {stderr.strip()}"
+# #         return stdout.strip()  # Assuming the model outputs a valid JSON string
+# #     except Exception as e:
+# #         return f"Unexpected error: {str(e)}"
+
+# # Gradio interface
+# def launch_gradio():
+#     with gr.Blocks(theme=gr.themes.Default()) as demo:
+#         gr.Markdown("# Enhanced Video to SOAP Note Generator")
+
+#         with gr.Tab("Audio/Video File to SOAP"):
+#             gr.Interface(
+#                 fn=process_file,
+#                 inputs=[gr.File(label="Upload Audio/Video File"), gr.Textbox(label="Enter Prompt for Template", placeholder="Enter a detailed prompt...", lines=6)],
+#                 outputs=[
+#                     gr.Textbox(label="SOAP Note"),
+#                     # gr.Textbox(label="Generated Template from LLaMA"),
+#                     # gr.Textbox(label="JSON Output")
+#                 ],
+#             )
+
+#         with gr.Tab("Text Input to SOAP"):
+#             gr.Interface(
+#                 fn=process_text,
+#                 inputs=[gr.Textbox(label="Enter Text", placeholder="Enter medical notes...", lines=6), gr.Textbox(label="Enter Prompt for Template", placeholder="Enter a detailed prompt...", lines=6)],
+#                 outputs=[
+#                     gr.Textbox(label="SOAP Note"),
+#                     # gr.Textbox(label="Generated Template from LLaMA"),
+#                     # gr.Textbox(label="JSON Output")
+#                 ],
+#             )
+
+#     demo.launch(share=True, debug=True)
+
+# # Run the Gradio app
+# if __name__ == "__main__":
+#     launch_gradio()