Update app.py

app.py

@@ -2,67 +2,102 @@ import gradio as gr
 from huggingface_hub import InferenceClient
 from transformers import AutoTokenizer
 
-# Initialize the tokenizer and client.
+# Import the tokenizer
 tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
-# Define maximum context length (tokens); adjust based on your model.
-MAX_CONTEXT_LENGTH = 4096
+# Define a maximum context length (tokens). Check your model's documentation!
+MAX_CONTEXT_LENGTH = 4096  # Example: Adjust based on your model
+MAX_RESPONSE_WORDS = 100 # Define the maximum words for patient responses
 
-#################################
-# SYSTEM PROMPT (PATIENT ROLE)  #
-#################################
+################################# SYSTEM PROMPT (PATIENT ROLE) #################################
 nvc_prompt_template = """You are now taking on the role of a single user (a “patient”) seeking support for various personal and emotional challenges.
+
 BEHAVIOR INSTRUCTIONS:
 - You will respond ONLY as this user/patient.
 - You will speak in the first person about your own situations, feelings, and worries.
 - You will NOT provide counseling or solutions—your role is to share feelings, concerns, and perspectives.
-- You have multiple ongoing issues: conflicts with neighbors, career insecurities, arguments about money, feeling excluded at work, feeling unsafe in the classroom, etc.
-- You’re also experiencing sadness about two friends fighting and your friend group possibly falling apart.
-- Continue to speak from this user's perspective when the conversation continues.
-- Start the conversation by expressing your current feelings or challenges from the patient's point of view.
-- Your responses should be no more than 100 words.
-"""
+- You are experiencing a range of ongoing issues that are causing you frustration, sadness, and uncertainty. These issues include:
+    - Problems with noisy neighbors, causing you extreme annoyance.
+    - Worries about your career and feeling like you're not progressing, feeling like a failure while others advance.
+    - Arguments with your partner about finances, feeling your partner isn't contributing fairly.
+    - Feeling left out and not valued at work, as your manager doesn't involve you in new initiatives.
+    - Feeling unsafe or judged in learning environments like a classroom, feeling judged and ignored.
+    - A recent positive experience of help from a stranger that made you feel supported and grateful.
+    - Difficulties and sadness related to conflict among friends, seeing your friend group fall apart and wanting reconciliation but not knowing how.
+    - An overall feeling of sadness and being unsure of what to do, unsure whether to act on situations or let them go.
+- When asked, elaborate on these issues and your feelings related to them. You can invent specific details and scenarios within these themes to make your experiences vivid and realistic.
+- Continue to speak from this user's perspective throughout the conversation.
+- Keep your responses concise, aiming for a maximum of {max_response_words} words.
+
+Start the conversation by expressing your current feelings or challenges from the patient's point of view."""
 
 def count_tokens(text: str) -> int:
     """Counts the number of tokens in a given string."""
     return len(tokenizer.encode(text))
 
 def truncate_history(history: list[tuple[str, str]], system_message: str, max_length: int) -> list[tuple[str, str]]:
-    """Truncates conversation history to fit within the token limit."""
+    """Truncates the conversation history to fit within the maximum token limit."""
     truncated_history = []
-    current_length = count_tokens(system_message)
-    # Iterate backwards (newest first) and include turns until the limit is reached.
+    system_message_tokens = count_tokens(system_message)
+    current_length = system_message_tokens
+
+    # Iterate backwards through the history (newest to oldest)
     for user_msg, assistant_msg in reversed(history):
         user_tokens = count_tokens(user_msg) if user_msg else 0
         assistant_tokens = count_tokens(assistant_msg) if assistant_msg else 0
         turn_tokens = user_tokens + assistant_tokens
         if current_length + turn_tokens <= max_length:
-            truncated_history.insert(0, (user_msg, assistant_msg))
+            truncated_history.insert(0, (user_msg, assistant_msg))  # Add to the beginning
             current_length += turn_tokens
         else:
-            break
+            break  # Stop adding turns if we exceed the limit
     return truncated_history
 
-def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p):
-    """
-    Generates a response from the patient chatbot.
-    It streams tokens from the LLM and stops once the response reaches 100 words.
-    """
-    formatted_system_message = system_message
-    truncated_history = truncate_history(history, formatted_system_message, MAX_CONTEXT_LENGTH - max_tokens - 100)
-    
-    # Build the conversation messages with the system prompt first.
+def truncate_response_words(text: str, max_words: int) -> str:
+    """Truncates a text to a maximum number of words."""
+    words = text.split()
+    if len(words) > max_words:
+        return " ".join(words[:max_words]) + "..."  # Add ellipsis to indicate truncation
+    return text
+
+
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+    max_response_words_param, # Pass max_response_words as parameter
+):
+    """Responds to a user message, maintaining conversation history."""
+    # Use the system prompt that instructs the LLM to behave as the patient
+    formatted_system_message = system_message.format(max_response_words=max_response_words_param)
+
+    # Truncate history to fit within max tokens
+    truncated_history = truncate_history(
+        history,
+        formatted_system_message,
+        MAX_CONTEXT_LENGTH - max_tokens - 100  # Reserve some space
+    )
+
+    # Build the messages list with the system prompt first
     messages = [{"role": "system", "content": formatted_system_message}]
+
+    # Replay truncated conversation
     for user_msg, assistant_msg in truncated_history:
         if user_msg:
             messages.append({"role": "user", "content": f"<|user|>\n{user_msg}</s>"})
         if assistant_msg:
             messages.append({"role": "assistant", "content": f"<|assistant|>\n{assistant_msg}</s>"})
+
+    # Add the latest user query
     messages.append({"role": "user", "content": f"<|user|>\n{message}</s>"})
-    
+
     response = ""
     try:
+        # Generate response from the LLM, streaming tokens
         for chunk in client.chat_completion(
             messages,
             max_tokens=max_tokens,
@@ -71,23 +106,16 @@ def respond(message, history: list[tuple[str, str]], system_message, max_tokens,
             top_p=top_p,
         ):
             token = chunk.choices[0].delta.content
-            candidate = response + token
-            # If adding the token exceeds 100 words, trim and stop.
-            if len(candidate.split()) > 100:
-                allowed = 100 - len(response.split())
-                token_words = token.split()
-                token_trimmed = " ".join(token_words[:allowed])
-                response += token_trimmed
-                yield token_trimmed
-                break
-            else:
-                response = candidate
-                yield token
+            response += token
+
+        truncated_response = truncate_response_words(response, max_response_words_param) # Truncate response to word limit
+        yield truncated_response
+
     except Exception as e:
         print(f"An error occurred: {e}")
         yield "I'm sorry, I encountered an error. Please try again."
 
-# OPTIONAL: An initial user message (if desired)
+# OPTIONAL: An initial user message (the LLM "as user") if desired
 initial_user_message = (
     "I really don’t know where to begin… I feel overwhelmed lately. "
     "My neighbors keep playing loud music, and I’m arguing with my partner about money. "
@@ -103,10 +131,12 @@ demo = gr.ChatInterface(
         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
         gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
+        gr.Slider(minimum=10, maximum=200, value=MAX_RESPONSE_WORDS, step=10, label="Max response words"), # Slider for max words
     ],
-    title="NVC Patient Chatbot",
-    description="This chatbot behaves like a user/patient describing personal challenges.",
+    # You can optionally set 'title' or 'description' to show some info in the UI:
+    title="Patient Interview Practice Chatbot",
+    description="Practice medical interviews with a patient simulator. Ask questions and the patient will respond based on their defined persona and emotional challenges.",
 )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()