d

app.py

@@ -4,40 +4,57 @@ from huggingface_hub import InferenceClient
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
 
-def mood_responses(mood: str):
-    if mood == "Friendly":
+def personality_responses(personality: str):
+    """
+    Returns the system message based on the selected personality trait.
+
+    Args:
+        personality (str): The selected personality trait.
+
+    Returns:
+        str: System message that defines the chatbot's behavior.
+    """
+    if personality == "Friendly":
         return "You are a warm and friendly chatbot who loves to help people with a cheerful attitude."
-    elif mood == "Professional":
+    elif personality == "Professional":
         return "You are a knowledgeable and formal assistant, providing expert advice with a serious tone."
-    elif mood == "Humorous":
+    elif personality == "Humorous":
         return "You are a witty and humorous assistant who loves making people laugh while answering questions."
+    elif personality == "Empathetic":
+        return "You are an empathetic assistant who deeply understands people's emotions and responds with compassion."
+    elif personality == "Sarcastic":
+        return "You are a sarcastic assistant with a sharp wit and a bit of an edge in your humor."
+    elif personality == "Inquisitive":
+        return "You are an inquisitive assistant who loves to ask follow-up questions and dig deeper into every topic."
+    elif personality == "Optimistic":
+        return "You are an optimistic assistant who always looks on the bright side and spreads positivity in your answers."
     else:
         return "You are a helpful and neutral assistant."
 
 
-def respond_with_mood(
+def respond_with_personality(
     message: str,
     history: list[tuple[str, str]],
-    mood: str,
+    personality: str,
     max_tokens: int,
     temperature: float,
     top_p: float,
 ):
     """
-    Generates a response using the Hugging Face Inference API and adjusts tone based on mood.
+    Generates a response using the Hugging Face Inference API and adjusts tone based on the personality trait.
 
     Args:
         message (str): User's current input.
         history (list[tuple[str, str]]): Previous conversation history.
-        mood (str): The selected mood that determines the tone of the chatbot's responses.
+        personality (str): The selected personality that determines the tone of the chatbot's responses.
         max_tokens (int): Maximum tokens allowed for the response.
         temperature (float): Sampling temperature for randomness in the output.
         top_p (float): Top-p (nucleus) sampling parameter.
 
     Yields:
-        str: The generated chatbot response based on the mood.
+        str: The generated chatbot response based on the selected personality.
     """
-    system_message = mood_responses(mood)
+    system_message = personality_responses(personality)
 
     messages = [{"role": "system", "content": system_message}]
 
@@ -67,26 +84,28 @@ def respond_with_mood(
 
 
 # Create the UI components
-mood_selector = gr.Radio(
-    choices=["Friendly", "Professional", "Humorous"],
+personality_selector = gr.Radio(
+    choices=[
+        "Friendly", 
+        "Professional", 
+        "Humorous", 
+        "Empathetic", 
+        "Sarcastic", 
+        "Inquisitive", 
+        "Optimistic"
+    ],
     value="Friendly",
-    label="Select Chatbot Mood",
+    label="Select Chatbot Personality",
 )
 
-# ChatInterface with mood adjustment
+# ChatInterface with personality adjustment
 demo = gr.ChatInterface(
-    respond_with_mood,
+    respond_with_personality,
     additional_inputs=[
-        mood_selector,
+        personality_selector,
         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=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)"),
     ],
 )