Update app.py

app.py

@@ -1,6 +1,6 @@
 from huggingface_hub import InferenceClient
 import gradio as gr
-import random
+from datetime import datetime
 
 API_URL = "https://api-inference.huggingface.co/models/"
 
@@ -16,24 +16,19 @@ def format_prompt(message, history):
     prompt += f"[INST] {message} [/INST]"
     return prompt
 
-def generate(prompt, history, temperature=0.9, max_new_tokens=512, top_p=0.95, repetition_penalty=1.0):
+def generate(prompt, history):
     """Generate a response using the text generation model."""
-    # Ensure temperature is not too low
-    temperature = max(float(temperature), 1e-2)
-    top_p = float(top_p)
-
     # Check if the prompt is asking who created the bot
     if "who created you" in prompt.lower():
         return "I was created by Aniket Kumar and many more."
 
     # Set up parameters for text generation
     generate_kwargs = dict(
-        temperature=temperature,
-        max_new_tokens=max_new_tokens,
-        top_p=top_p,
-        repetition_penalty=repetition_penalty,
+        temperature=0.9,
+        max_new_tokens=512,
+        top_p=0.95,
+        repetition_penalty=1.0,
         do_sample=True,
-        seed=random.randint(0, 10**7),
     )
 
     # Format the prompt
@@ -47,47 +42,19 @@ def generate(prompt, history, temperature=0.9, max_new_tokens=512, top_p=0.95, r
         yield output
     return output
 
+def greet_user():
+    """Greet the user based on the time of day."""
+    current_hour = datetime.now().hour
+
+    if current_hour < 12:
+        return "Good morning! How can I assist you today?"
+    elif 12 <= current_hour < 18:
+        return "Good afternoon! How can I assist you today?"
+    else:
+        return "Good evening! How can I assist you today?"
+
 def create_interface():
     """Create the Gradio interface."""
-    additional_inputs=[
-        gr.Slider(
-            label="Temperature",
-            value=0.9,
-            minimum=0.0,
-            maximum=1.0,
-            step=0.05,
-            interactive=True,
-            info="Higher values produce more diverse outputs",
-        ),
-        gr.Slider(
-            label="Max new tokens",
-            value=512,
-            minimum=64,
-            maximum=1024,
-            step=64,
-            interactive=True,
-            info="The maximum numbers of new tokens",
-        ),
-        gr.Slider(
-            label="Top-p (nucleus sampling)",
-            value=0.90,
-            minimum=0.0,
-            maximum=1,
-            step=0.05,
-            interactive=True,
-            info="Higher values sample more low-probability tokens",
-        ),
-        gr.Slider(
-            label="Repetition penalty",
-            value=1.2,
-            minimum=1.0,
-            maximum=2.0,
-            step=0.05,
-            interactive=True,
-            info="Penalize repeated tokens",
-        )
-    ]
-
     customCSS = """
     #component-7 { # this is the default element ID of the chat component
       height: 800px; # adjust the height as needed
@@ -98,7 +65,7 @@ def create_interface():
     with gr.Blocks(css=customCSS) as demo:
         gr.ChatInterface(
             generate,
-            additional_inputs=additional_inputs,
+            initial_message=greet_user(),  # Add the greeting feature here
         )
 
     demo.queue().launch(debug=True)