Update app.py

app.py

@@ -13,9 +13,10 @@ import tempfile
 import os
 import boto3
 from gradio import Interface, components as gr
-from gradio import Interface, Textbox, Audio, Radio
+from gradio import Interface
 import io
 from scipy.io import wavfile
+from google.cloud import speech
 import pyttsx3
 from nltk.tokenize import sent_tokenize
 import nltk
@@ -44,8 +45,7 @@ def construct_index(directory_path):
 
     return index
     
-   
-def transcribe_audio(audio):
+def transcribe_audio(audio, service="Google"):
     sampling_rate, audio_data = audio  # unpack the tuple
 
     if audio_data.ndim > 1:
@@ -62,32 +62,38 @@ def transcribe_audio(audio):
         r = sr.Recognizer()
         with sr.AudioFile(fp.name) as source:
             audio_data = r.record(source)
-            try:
-                with open(fp.name, "rb") as audio_file:
-                    transcript = openai.Audio.transcribe("whisper-1", audio_file)
-
-                print(transcript)
-
-                conversation = [{"role": "user", "content": transcript["text"]}]
-
-                response = openai.ChatCompletion.create(
-                    model="gpt-3.5-turbo",
-                    messages=conversation
-                )
-
-                print(response)
-                text = transcript["text"]
-                
-            except Exception as e:
-                print("Error with Whisper Service:", str(e))
-                text = sent_tokenize(text)                    
+            if service == "Google":
+                try:
+                    text = r.recognize_google(audio_data)
+                except sr.RequestError as e:
+                    print(f"Could not request results from Google Speech Recognition service; {e}")
+                except sr.UnknownValueError:
+                    print("Google Speech Recognition could not understand audio")
+                    text = sent_tokenize(text)
+            elif service == "Whisper":
+                try:
+                    with open(fp.name, "rb") as audio_file:
+                        transcript = openai.Audio.transcribe("whisper-1", audio_file)
+
+                    print(transcript)
+
+                    conversation = [{"role": "user", "content": transcript["text"]}]
+
+                    response = openai.ChatCompletion.create(
+                        model="gpt-3.5-turbo",
+                        messages=conversation
+                    )
+
+                    print(response)
+                    text = transcript["text"]
+                    
+                except Exception as e:
+                    print("Error with Whisper Service:", str(e))
+                    text = sent_tokenize(text)                    
     finally:
         os.unlink(fp.name)
 
     return text
-
-
-
 def get_gpt_response(input_text):
     try:
         # Check that input_text is not empty
@@ -95,7 +101,7 @@ def get_gpt_response(input_text):
             return "No input provided.", "", "", "", ""
     
         conversation = [
-            {"role": "system", "content": "You are an experienced medical consultant who provides a SOAP note based on the information in the input provided."},
+            {"role": "system", "content": "You are an experienced medical consultant who provides a SOAP note based on the conversation provided."},
             {"role": "user", "content": input_text}
         ]
         response = openai.ChatCompletion.create(
@@ -126,36 +132,32 @@ def get_gpt_response(input_text):
 
 
 
-def chatbot(input_text, input_voice, patient_name=None):
-
-    # Check if patient_name is in index
+def chatbot(input_text, input_voice, transcription_service, patient_name=None):
+# Check if patient_name is in index
     index = GPTSimpleVectorIndex.load_from_disk('index.json')
     if patient_name:  # Only do the check if patient_name is not None and not an empty string
-        patient_names = [doc['name'] for doc in index.documents]  # Assuming each document is a dictionary with a 'name' field
+       patient_names = [doc['name'] for doc in index.documents]  # Assuming each document is a dictionary with a 'name' field
     if patient_name and patient_name not in patient_names:
-        return "", "", "", "", "", "", "", "", "", "Patient not found in index.", ""  # Fill the rest of the outputs with empty strings
+        return "Patient not found in index.", "", "", "", "", "", "", "", "", "", "", input_text  # Fill the rest of the outputs with empty strings
     if input_voice is not None:
-        input_text = transcribe_audio(input_voice)
+        input_text = transcribe_audio(input_voice, transcription_service)
 
     # Get a response from GPT-3.5-turbo
     gpt_subjective, gpt_objective, gpt_assessment, gpt_plan, gpt_general = get_gpt_response(input_text)
-    # Save GPT response to a file
-    gpt_file_path = os.path.join('GPTresponses/', f"{patient_name}.txt")
-    with open(gpt_file_path, "a") as f:
-        f.write(f"Subjective: {gpt_subjective}\nObjective: {gpt_objective}\nAssessment: {gpt_assessment}\nPlan: {gpt_plan}\nGeneral: {gpt_general}\n\n")
+
     index = GPTSimpleVectorIndex.load_from_disk('index.json')
     response_index = index.query(input_text, response_mode="compact")
 
     soap_response = response_index.response
 
     patient_name = soap_response.split(' ')[1] if 'Subjective:' in soap_response else 'General'
-    patient_file_path = os.path.join('Docs/', f"{patient_name}.txt")
+    patient_file_path = os.path.join('/home/user/app/Docs', f"{patient_name}.txt")
 
-    if all(keyword.lower() in soap_response.lower() for keyword in ["subjective:", "objective:", "assessment:", "plan:"]):
-        s_index = soap_response.lower().find('subjective:')
-        o_index = soap_response.lower().find('objective:')
-        a_index = soap_response.lower().find('assessment:')
-        p_index = soap_response.lower().find('plan:')
+    if all(keyword in soap_response for keyword in ["Subjective:", "Objective:", "Assessment:", "Plan:"]):
+        s_index = soap_response.find('Subjective:')
+        o_index = soap_response.find('Objective:')
+        a_index = soap_response.find('Assessment:')
+        p_index = soap_response.find('Plan:')
 
         subjective = soap_response[s_index:o_index].replace('Subjective:', '').strip()
         objective = soap_response[o_index:a_index].replace('Objective:', '').strip()
@@ -165,39 +167,43 @@ def chatbot(input_text, input_voice, patient_name=None):
         with open(patient_file_path, "a") as f:
             f.write(f"Subjective: {subjective}\nObjective: {objective}\nAssessment: {assessment}\nPlan: {plan}\n\n")
 
-        output = [f"\n\n\u2022 Subjective: {subjective}\n\n\u2022 Objective: {objective}\n\n\u2022 Assessment: {assessment}\n\n\u2022 Plan: {plan}", ""]
-
+        output = [subjective, objective, assessment, plan, ""]
     else:
         with open(patient_file_path, "a" , encoding='utf-8') as f:
             f.write(f"General: {soap_response}\n\n")
 
-        output = ["", soap_response]
-
-    return *output, f"Subjective: {gpt_subjective}\nObjective: {gpt_objective}\nAssessment: {gpt_assessment}\nPlan: {gpt_plan}", gpt_general, input_text  # return the transcribed text and the GPT response
-
-
+        output = ["", "", "", "", soap_response]
 
-from gradio import Interface, Textbox, Audio, Radio
+    return *output, gpt_subjective, gpt_objective, gpt_assessment, gpt_plan, gpt_general, input_text  # return the transcribed text and the GPT response
+#return *output, gpt_subjective, gpt_objective, gpt_assessment, gpt_plan, output[4] + gpt_general, input_text(this to merge the general (none SOAP) from Index and GPT) 
 
-from gradio import Interface, Textbox, Audio, Radio
+from gradio import Interface
+from gradio.inputs import Textbox, Audio, Radio
+from gradio.outputs import Textbox
 
 interface = Interface(
     fn=chatbot,
     inputs=[
         Textbox(label="Enter your text"),
         Audio(source="microphone", type="numpy", label="Speak Something"),
+        Radio(["Google", "Whisper"], label="Choose a transcription service")
     ],
     outputs=[
-        Textbox(label="SOAP Output"),
-        Textbox(label="General Output"),
-        Textbox(label="GPT SOAP Output"),
-        Textbox(label="GPT General Output"),
-        Textbox(label="Transcribed Text")
-    ],
-)
+    Textbox(label="Subjective"),
+    Textbox(label="Objective"),
+    Textbox(label="Assessment"),
+    Textbox(label="Plan"),
+    Textbox(label="General"),
+    Textbox(label="GPT Subjective"),
+    Textbox(label="GPT Objective"),
+    Textbox(label="GPT Assessment"),
+    Textbox(label="GPT Plan"),
+    Textbox(label="GPT General"),
+    Textbox(label="Transcribed Text"),  # window for the transcribed text
+],
 
-
-index = construct_index('Docs/')
+)
+index = construct_index('/home/user/app/Docs')
 interface.launch()