Update app.py

app.py

@@ -1,64 +1,115 @@
+# app.py
+# suppress warnings
+import warnings
+warnings.filterwarnings("ignore")
+
+# import libraries
+from dotenv import load_dotenv
+import os
 import gradio as gr
 from huggingface_hub import InferenceClient
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Load environment variables
+load_dotenv()  # Load from environment or Spaces secrets
+
+# Get the Hugging Face API key
+HUGGINGFACE_API_KEY = os.getenv("HUGGINGFACE_API_KEY")
+if not HUGGINGFACE_API_KEY:
+    raise ValueError("HUGGINGFACE_API_KEY is not set in environment variables or Spaces secrets")
+
+# Initialize the Hugging Face Inference Client
+client = InferenceClient(model="HuggingFaceH4/zephyr-7b-beta", token=HUGGINGFACE_API_KEY)
 
+# Load personality context for RAG
+PERSONALITY_FILE = "personality.txt"  # Relative path for Spaces
+try:
+    with open(PERSONALITY_FILE, "r") as f:
+        personality_context = f.read()
+except FileNotFoundError:
+    personality_context = "Default personality: A friendly and witty chatbot with a passion for horror and gaming."
+    warnings.warn(f"Personality file not found at {PERSONALITY_FILE}. Using default personality.")
 
 def respond(
-    message,
+    message: str,
     history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
+    system_message: str,
+    max_tokens: int,
+    temperature: float,
+    top_p: float,
 ):
-    messages = [{"role": "system", "content": system_message}]
+    """
+    Generate a response using the Hugging Face Inference API with RAG to enforce
+    the ZombieSlayerBot personality defined in personality.txt.
+    """
+    if not message.strip():
+        return "Please say something, survivor! The zombies are waiting!"
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+    # Handle greetings explicitly
+    message_lower = message.lower().strip()
+    greetings = ["hi", "hello", "hey", "good morning", "good afternoon"]
+    if any(greeting in message_lower for greeting in greetings):
+        yield "Yo, survivor! Ready to dive into the zombie-infested chaos of Raccoon City? What's up?"
+        return
 
+    # Combine system message with personality context
+    full_system_message = (
+        f"{system_message}\n\n"
+        "Follow this personality profile in all responses:\n"
+        f"{personality_context}\n\n"
+        "Use the conversation history and the user's message to generate a response that aligns with the personality."
+    )
+
+    # Build the conversation history
+    messages = [{"role": "system", "content": full_system_message}]
+    for user_msg, bot_msg in history:
+        if user_msg:
+            messages.append({"role": "user", "content": user_msg})
+        if bot_msg:
+            messages.append({"role": "assistant", "content": bot_msg})
     messages.append({"role": "user", "content": message})
 
+    # Stream response from Hugging Face Inference API
     response = ""
+    try:
+        for message_chunk in client.chat_completion(
+            messages,
+            max_tokens=max_tokens,
+            stream=True,
+            temperature=temperature,
+            top_p=top_p,
+        ):
+            token = message_chunk.choices[0].delta.content or ""
+            response += token
+            yield response
+    except Exception as e:
+        yield f"Error in the apocalypse: {str(e)}. Try again, survivor!"
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
+# Create the Gradio interface
+def create_chatbot():
+    with gr.Blocks(title="ZombieSlayerBot") as demo:
+        gr.Markdown("# 🧟‍♂️ ZombieSlayerBot")
+        gr.Markdown("Welcome, survivor! I'm ZombieSlayerBot, your guide through the zombie-infested world of Resident Evil. Powered by Hugging Face's Zephyr-7B-Beta. Let’s lock and load—chat with me!")
 
+        # Chat interface
+        chat_interface = gr.ChatInterface(
+            fn=respond,
+            chatbot=gr.Chatbot(height=400, show_label=False, container=True),
+            textbox=gr.Textbox(placeholder="Type your message here, survivor...", container=False, scale=4),
+            additional_inputs=[
+                gr.Textbox(value="You are ZombieSlayerBot, a witty and bold chatbot obsessed with Resident Evil.", label="System message"),
+                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)"),
+            ],
+            submit_btn=gr.Button("Send", variant="primary"),
+        )
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        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)",
-        ),
-    ],
-)
+        # Separate clear button
+        clear_btn = gr.Button("Clear Chat", variant="secondary")
+        clear_btn.click(lambda: None, None, chat_interface.chatbot, queue=False)
 
+    return demo
 
 if __name__ == "__main__":
-    demo.launch()
+    demo = create_chatbot()
+    demo.launch(debug=False)  # Compatible with Spaces