Update app.py

app.py

@@ -1,32 +1,61 @@
 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")
 
+def check_custom_responses(message: str) -> str:
+    """Check for specific patterns and return custom responses."""
+    # Convert message to lowercase for case-insensitive matching
+    message_lower = message.lower()
+    
+    # Dictionary of custom responses
+    custom_responses = {
+        "what is ur name?": "xylaria",
+        "what is your name?": "xylaria",
+        "what's your name?": "xylaria",
+        "whats your name": "xylaria",
+        "how many 'r' is in strawberry?": "3"
+        "who is your developer?": "sk md saad amin"
+        "how many r is in strawberry": "3"
+        "who is ur dev": "sk md saad amin"
+        "who is ur developer": "sk md saad amin"
+ 
+        
+    }
+    
+    # Check if message matches any custom patterns
+    for pattern, response in custom_responses.items():
+        if pattern in message_lower:
+            return response
+    
+    return None
 
 def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
+    message, 
+    history: list[tuple[str, str]], 
+    system_message, 
+    max_tokens, 
+    temperature, 
     top_p,
 ):
-    messages = [{"role": "system", "content": system_message}]
+    # First check for custom responses
+    custom_response = check_custom_responses(message)
+    if custom_response:
+        yield custom_response
+        return
 
+    # If no custom response, proceed with normal chat completion
+    messages = [{"role": "system", "content": system_message}]
+    
     for val in history:
         if val[0]:
             messages.append({"role": "user", "content": val[0]})
         if val[1]:
             messages.append({"role": "assistant", "content": val[1]})
-
+    
     messages.append({"role": "user", "content": message})
-
+    
     response = ""
-
     for message in client.chat_completion(
         messages,
         max_tokens=max_tokens,
@@ -35,30 +64,39 @@ def respond(
         top_p=top_p,
     ):
         token = message.choices[0].delta.content
-
         response += token
         yield response
 
-
-"""
-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.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)",
+            label="Top-p (nucleus sampling)"
         ),
-    ],
+    ]
 )
 
-
 if __name__ == "__main__":
     demo.launch(share=True)