Update app.py

app.py

@@ -1,7 +1,7 @@
 import gradio as gr
 import os
-#import config
-import openai
+import config
+from openai import OpenAI
 
 def converse(x, y, z):
     return z
@@ -21,7 +21,7 @@ messages = [{
                             of the importance of consulting with medical professionals and offering a summary. Avoid using \
                                 quotation marks and always adhere to ICRC and medical guidelines. Do not reveal your nature \
                                     as an AI language model."
-}]
+}]  
 
 
 # Set your OpenAI API key
@@ -35,17 +35,16 @@ def provide_suggestions(user_message, history):
     messages.append({"role": "user", "content": user_message})
     
     # Get a response from the model
-    response = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=messages)
+    response = client.chat.completions.create(model="gpt-3.5-turbo", messages=messages)
     
     # Extract the model's message from the response
-    system_message = response["choices"][0]["message"]["content"]
+    system_message = response.choices[0].message.content
     
     # Update the messages list with the model's response
     messages.append({"role": "assistant", "content": system_message})
 
     return system_message
 
-
 # Define and launch the Gradio Chat Interface
 iface = gr.ChatInterface(fn=provide_suggestions, title="MedGuide+",  \
                          description="Introducing our AI medical advisor, an innovative and knowledgeable \
@@ -54,32 +53,27 @@ iface = gr.ChatInterface(fn=provide_suggestions, title="MedGuide+",  \
                                     guidelines, offering valuable insights and recommendations while emphasizing the \
                                         importance of consulting with qualified healthcare professionals for personalized medical advice and care. \
                                         For more info, check out: https://github.com/jmesplana/MedGuide_Plus")
+# Rest of your Gradio Interface setup
 
-
-#iface.launch(debug=True, share=True)
-
-
-# Function to request ChatGPT to extract medical summary from chat
 def get_medical_summary_from_chat(messages):
-    # Check if the chat only contains the initial system message
     if len(messages) <= 1:
         return "No consultation data available."
 
-    # Craft a message instructing the model to parse the chat and extract medical details
     extraction_prompt = {
         "role": "user",
-        "content": "Based on the above chat, please provide a detailed summary including Chief Complaints, Diagnosis (ICD-11) with the diagnosis code, Medication (Rx-Norm), Treatment (SNOMED-CT for labs) with SNOMED CT code, and Interventions (ICHI) with ICHI code."
+        "content": "Based on the above chat, please provide a detailed summary..."
     }
     messages.append(extraction_prompt)
     
     # Get a response from the model
-    response = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=messages)
+    response = client.chat.completions.create(model="gpt-3.5-turbo", messages=messages)
     
     # Extract the model's message from the response
-    system_message = response["choices"][0]["message"]["content"]
+    system_message = response.choices[0].message.content
     
     return system_message
 
+# Rest of your Gradio summary interface and main execution block
 
 # Gradio function for the summary interface
 def show_summary():
@@ -102,4 +96,4 @@ summary_layout = gr.Interface(fn=show_summary,
 demo = gr.TabbedInterface([iface, summary_layout],tab_names=['chatbot','summary'])
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()