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app.py

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+# Simple APP for specialty pharmacy 
+
+# Import packages
+import numpy as np 
+import os 
+
+import gradio as gr
+from transformers import pipeline
+
+#Import LLMs 
+from langchain.llms import OpenAI
+from langchain.chat_models import ChatOpenAI
+
+# Prompt template 
+from langchain import PromptTemplate
+
+# Chains
+from langchain.chains import LLMChain
+
+
+# Import "secret" OPENAI_API_KEY
+os.environ["OPENAI_API_KEY"] 
+
+# Import GPT-4
+llm_gpt = ChatOpenAI(model='gpt-4-0613',temperature=0.)
+
+# ======================================================
+# Set up an ASR pipeline using facebook's wav2vec2
+p = pipeline("automatic-speech-recognition", chunk_length_s=40)
+
+# =======================================================
+# LLM Chains
+
+# Dialogue chain
+template_diag = """
+You are an AI assistant with medical language understanding.
+
+The input is a dialogue between a specialty pharmacist and patient: {input}
+
+To give you context, the dialogue will have to do about symptoms, side effects, medications etc
+of a rare disease, most probably multiple sclerosis.
+
+You have a couple of tasks:
+
+- First: If there are some non-sensical words, convert them to the most probable real word,
+taking into account that this is a pharmaxist, so most of them should describe medical conditions
+or symptoms, most probably about multiple sclerosis.
+If a medication is mentioned, do your best to find which is that, if any. Correct any mispellings
+Capitalize the names of the medications.
+
+- Second: Convert the text into a dialogue of the form:
+
+[Pat]: 
+[PRx]:
+
+Where [PRx]: Pharmacist, [Pat]: Patient
+
+Use your judgement to distinguish between the two roles and who said what.
+Output only this dialogue.
+
+Output:
+"""
+
+
+prompt_diag = PromptTemplate(template=template_diag, input_variables=["input"])
+chain_diag = LLMChain(llm=llm_gpt, prompt=prompt_diag, verbose=False)
+
+
+# ==============================================
+template_struct = """
+You are an AI assistant with medical language understanding.
+
+The input is a dialogue between a specialty pharmacist and patient: {input}
+
+To give you context, the dialogue will have to do about symptoms, side effects, medications etc
+of a rare disease, most probably multiple sclerosis.
+
+Some words may not be clearly spelled, because they come from an automatic 
+audio to text transcript. 
+
+Your have a few tasks:
+
+- First task: If there are some non-sensical words, convert them to the most probable real word,
+taking into account that this is a dialogue about a medical condition, probably multiple sclerosis
+
+- Second task: extract information from this dialogue
+
+Specifically the following:
+
+- A brief summary of the dialogue, highlighting the chief complaint 
+- The main disease mentioned by the patient
+- Medications mentioned by the patient
+- Side effets mentioned by the patient
+
+The output should have the form of a json file with those four keys: (Summary, Disease, Medications, Side_Effects)
+
+Do not hallucinate and do not make up information that is not included in the original file.
+
+Output:
+"""
+
+# SOAP notes 
+prompt_struct = PromptTemplate(template=template_struct, input_variables=["input"])
+chain_struct = LLMChain(llm=llm_gpt, prompt=prompt_struct, verbose=False)
+
+
+# Transcription function
+def transcribe(audio):
+    #text = fake_audio
+    text = p(audio)["text"]
+    output_1 = eval(chain_struct.run(text))
+    output_2 = chain_diag.run(text)
+    summa = output_1['Summary']
+    disease = output_1['Disease']
+    meds = output_1['Medications']
+    sides = output_1['Side_Effects']
+    return summa, disease, meds, sides, output_2  
+        
+    
+# 
+with gr.Blocks(title="AI specialty scriber",theme=gr.themes.Soft()) as demo:
+    
+    with gr.Row():
+        image_wag = gr.Image(value="Walgreens_AI.png",height=100, width=100, show_label=False,show_download_button=False, scale=1)
+    gr.Markdown("## <center> Walgreens AI-powered specialty pharmacy tool </center>")
+    #gr.Markdown("**<center>"+scriber_description+"</center>**")
+    gr.Markdown("<center> ________________________________________________________________________ </center>")
+    
+    # ====================================================
+    # Dictation tool
+    gr.Markdown("**Record Patient Interaction**")
+    audio = gr.Audio(label='Your recording here',source="microphone", type="filepath",container=True)
+    audio_submit_btn = gr.Button(value="Submit Recording", variant="primary")
+    
+    # Clinical notess and transcript
+    with gr.Tab("Extracted Information"):
+        with gr.Row():
+            summary = gr.Textbox(label='Summary',lines=3,interactive=True) 
+            disease = gr.Textbox(label='Disease mentioned',lines=3,interactive=True)
+        with gr.Row():
+            medications = gr.Textbox(label='Medications mentioned',lines=3,interactive=True)
+            sides = gr.Textbox(label='Side Effects mentioned',lines=3,interactive=True)
+        
+    with gr.Tab("Original Transcript"):
+        dialogue = gr.Textbox(label='Full conversation transcript',lines=10)
+        
+    # ===============================================
+    # Submit and clear tool    
+    audio_submit_btn.click(transcribe, inputs = audio, outputs=[summary,disease,medications,sides,dialogue])
+    audio_clear_btn = gr.ClearButton([audio,summary,disease,medications,sides,dialogue])
+    
+    
+demo.launch(auth=("alliancedemo","wag2046"))

requirements.txt

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+gradio==3.40.0
+langchain==0.0.178
+openai==0.27.6
+transformers==4.28.1
+torch==2.0.1