c

app.py

@@ -1,55 +1,49 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
-from typing import List, Tuple, Dict
 
+"""
+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("AuriLab/gpt-bi-instruct-cesar")
 
-def format_messages(history: List[Tuple[str, str]], system_message: str, user_message: str) -> List[Dict[str, str]]:
-    messages = [{"role": "system", "content": system_message}]
-    messages.extend([
-        {"role": "user" if i % 2 == 0 else "assistant", "content": str(msg)}  # Convert msg to string
-        for turn in history
-        for i, msg in enumerate(turn)
-        if msg is not None
-    ])
-    messages.append({"role": "user", "content": str(user_message)})  # Convert user_message to string
-    return messages
-
-async def respond(message: str, history: List[Tuple[str, str]]) -> str:
-    system_message = "You are a helpful AI assistant."
-    max_tokens = 1000
-    temperature = 0.7
-    top_p = 0.85
-    
-    messages = format_messages(history, system_message, message)
+
+def respond(
+    message,
+    history: list[tuple[str, str]],
+):
+    messages = [{"role": "system", "content": "Gpt-Bi zara, AuriLabsek sortutako assitente digitala."}]
+
+    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 = ""
-    
-    try:
-        async for msg in client.chat_completion(
-            messages,
-            max_tokens=max_tokens,
-            stream=True,
-            temperature=temperature,
-            top_p=top_p,
-        ):
-            if hasattr(msg.choices[0].delta, 'content'):
-                token = msg.choices[0].delta.content
-                if token is not None:
-                    response += token
-                    yield response
-        
-        if not response:  # Handle empty response case
-            yield "No response generated."
-            
-    except Exception as e:
-        yield f"Error: {str(e)}"
-
-# Update the ChatInterface to use async function
+
+    for message in client.chat_completion(
+        messages,
+        max_tokens=200,
+        stream=True,
+        temperature=0.7,
+        top_p=50,
+    ):
+        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(
-    fn=respond,
-    title="Demo GPT-BI instruct",
-    examples=["nola duzu izena?", "Nola egiten duzu?"]
+    respond,
+    title="GPT-BI Instruct",
 )
 
+
 if __name__ == "__main__":
-    demo.launch(share=False)
+    demo.launch()