Create patient2.py

patient2.py

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+import gradio as gr
+from huggingface_hub import InferenceClient
+from transformers import AutoTokenizer
+
+# Import the tokenizer
+tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+
+# 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) #################################
+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 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 the conversation history to fit within the maximum token limit."""
+    truncated_history = []
+    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))  # Add to the beginning
+            current_length += turn_tokens
+        else:
+            break  # Stop adding turns if we exceed the limit
+    return truncated_history
+
+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,
+            stream=True,
+            temperature=temperature,
+            top_p=top_p,
+        ):
+            token = chunk.choices[0].delta.content
+            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 (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. "
+    "Also, two of my friends are fighting, and the group is drifting apart. "
+    "I just feel powerless."
+)
+
+# --- Gradio Interface ---
+demo = gr.ChatInterface(
+    fn=respond,
+    additional_inputs=[
+        gr.Textbox(value=nvc_prompt_template, label="System message", visible=True),
+        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
+    ],
+    # 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()