Update app.py

app.py

@@ -2,6 +2,7 @@ import gradio as gr
 import os
 from openai import OpenAI
 import os.path
+from datetime import datetime
 
 ################# Start PERSONA-SPECIFIC VALUES ######################
 coach_code = "hi"
@@ -9,7 +10,7 @@ coach_name_short = "Hannah"
 coach_name_upper = "HANNAH"
 coach_name_long = "Hannah"
 sys_prompt_new = os.getenv("PROMPT_NEW")
-theme="ParityError/Anime"
+theme='ParityError/Anime'
 ################# End PERSONA-SPECIFIC VALUES ######################
 
 ################# Start OpenAI-SPECIFIC VALUES ######################
@@ -19,22 +20,38 @@ client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
 openai_model = "gpt-3.5-turbo-0125"
 ################# End OpenAI-SPECIFIC VALUES ######################
 
-###############  CHAT  ###################
+tx = os.getenv("TX")
+prefix = ""  #  "data/" if local or "/data/" if persistent in HF
+
+############### CHAT ###################
 def predict(user_input, history):
     max_length = 500
-    if len(user_input) > max_length:
+    transcript_file_path = f"{prefix}{coach_code}-transcript.txt"
+    transcript = ""  # Initialize the transcript variable
+
+    if user_input == tx:
+        try:
+            # Prepare the transcript for the Textbox output
+            if os.path.exists(transcript_file_path):
+                with open(transcript_file_path, "r", encoding="UTF-8") as file:
+                    transcript = file.read()
+                return transcript
+        except FileNotFoundError:
+            return "File 'transcript.txt' not found."
+    elif len(user_input) > max_length:
         raise gr.Error(f"Input is TOO LONG. Max length is {max_length} characters. Try again.")
+
     history_openai_format = [
-            {"role": "system", "content": "IDENTITY: " + sys_prompt_new}
-        ]
+        {"role": "system", "content": "IDENTITY: " + sys_prompt_new}
+    ]
     for human, assistant in history:
-        history_openai_format.append({"role": "user", "content": human })
-        history_openai_format.append({"role": "assistant", "content":assistant})
+        history_openai_format.append({"role": "user", "content": human})
+        history_openai_format.append({"role": "assistant", "content": assistant})
     history_openai_format.append({"role": "user", "content": user_input})
 
     completion = client.chat.completions.create(
         model=openai_model,
-        messages= history_openai_format,
+        messages=history_openai_format,
         temperature=1.2,
         frequency_penalty=0.4,
         presence_penalty=0.1,
@@ -42,11 +59,21 @@ def predict(user_input, history):
     )
 
     output_stream = ""
-    for chunk in completion:
-        if chunk.choices[0].delta.content is not None:
-            output_stream = output_stream + (chunk.choices[0].delta.content)
-            yield output_stream
-    message_content = output_stream
+    try:
+        for chunk in completion:
+            if chunk.choices[0].delta.content is not None:
+                output_stream = output_stream + (chunk.choices[0].delta.content)
+        message_content = output_stream
+    except StopAsyncIteration:
+        pass
+
+    # Append latest user and assistant messages to the transcript
+    transcript += "Date/Time: " + datetime.now().strftime("%Y-%m-%d %H:%M:%S") + "\n\n"
+    transcript += f"YOU: {user_input}\n\n"
+    transcript += f"{coach_name_upper}: {message_content}\n\n\n"
+    # Write the updated transcript to the file
+    with open(transcript_file_path, "a", encoding="UTF-8") as file:
+        file.write(transcript)
 
     return message_content