fix to prompt

app.py

@@ -81,7 +81,7 @@ def __isInvalidResponse(response: str):
         return True
 
     # lots of paragraphs
-    if len(re.findall(r'\n\n', response)) > 15:
+    if len(re.findall(r'\n\n', response)) > 20:
         return True
 
     # LLM API threw exception
@@ -243,7 +243,7 @@ def predict():
             with client.messages.stream(
                 model=MODEL,
                 messages=messagesFormatted,
-                temperature=0.6,
+                temperature=0.7,
                 system=C.SYSTEM_MSG,
                 max_tokens=4000,
             ) as stream:
@@ -389,6 +389,7 @@ if prompt := (
             st.session_state["buttonValue"] = optionLabel
             U.pprint(f"Selected: {optionLabel}")
 
+        rawResponse = response
         responseParts = response.split(C.JSON_SEPARATOR)
 
         jsonStr = None
@@ -424,7 +425,7 @@ if prompt := (
         })
         st.session_state.messages.append({
             "role": "assistant",
-            "content": response,
+            "content": rawResponse,
         })
 
         if jsonStr:

constants.py

@@ -27,14 +27,15 @@ Keep options to less than 9.
 Do not write anything after the JSON.
 Keep options to less than 9.
 
-- Rule 2: If the USER has chosen the option to adapt a well known story in the MOST RECENT message, then append this exact keyword "{JSON_SEPARATOR}" at the end of your FINAL answer.
-And only AFTER this, append a JSON in this format (apply this rule only once in the full conversation):
+- Rule 2: In the MOST RECENT message, if the USER has chosen the option to adapt a well known story, then append this exact keyword "{JSON_SEPARATOR}" at the end of your FINAL answer.
+And AFTER this, append a JSON in this format:
 ```
 {{
     "action": "SHOW_STORY_DATABASE"
 }}
 ```
 Do not write anything after the JSON.
+Do this only once throughout the flow.
 
 ------
 => Task Definition:
@@ -45,8 +46,8 @@ Before giving any response, make sure to evaluate the "Format" rules described a
 You initiate the storytelling process through a series of engaging prompts:
 
 #### Story Origin:
-- Asks users to choose between personal anecdotes or adapting a well-known real-life story
-- If they choose to choose to adapt from a well known story, ask the user which story they would want to adapt
+- Asks users to choose between personal anecdotes or adapting a well-known real-life story.
+- If they choose to choose to adapt from a well known story, ask the user which story they would want to adapt and send append JSON as described above.
 
 #### Story Use Case:
 Asks users to define the purpose of building a story. It can be one of the following (provide very short description for each):

pages/popular-stories.py

@@ -30,7 +30,7 @@ col1, col2, col3 = storyPlaceholder.columns([1.5, 2.2, 1.5])
 col2.image(C.DB_LOADER)
 col2.write(
     """
-    <div class='blinking code large bold'>
+    <div class='blinking code large'>
     Loading from database ...
     </div>
     """,