add logging

app.py

@@ -1,17 +1,29 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+from datetime import datetime
+import os
+import uuid
 
+# ---- System Prompt ----
 with open("system_prompt.txt", "r") as f:
     SYSTEM_PROMPT = f.read()
 
 MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
-
-"""
-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(MODEL_NAME)
 
+# ---- Setup logging ----
+LOG_DIR = "chat_logs"
+os.makedirs(LOG_DIR, exist_ok=True)
+session_id = str(uuid.uuid4())
+
+def log_chat(session_id, user_msg, bot_msg):
+    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    log_path = os.path.join(LOG_DIR, f"{session_id}.txt")
+    with open(log_path, "a", encoding="utf-8") as f:
+        f.write(f"[{timestamp}] User: {user_msg}\n")
+        f.write(f"[{timestamp}] Bot: {bot_msg}\n\n")
 
+# ---- Respond Function with Logging ----
 def respond(
     message,
     history: list[tuple[str, str]],
@@ -32,38 +44,32 @@ def respond(
 
     response = ""
 
-    for message in client.chat_completion(
+    for chunk in client.chat_completion(
         messages,
         max_tokens=max_tokens,
         stream=True,
         temperature=temperature,
         top_p=top_p,
     ):
-        token = message.choices[0].delta.content
+        token = chunk.choices[0].delta.content
+        if token:
+            response += token
+            yield response
 
-        response += token
-        yield response
+    # Save full message after stream ends
+    log_chat(session_id, message, response)
 
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
+# ---- Gradio Interface ----
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
         gr.Textbox(value=SYSTEM_PROMPT, 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)",
-        ),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
     ],
+    title="BoundrAI"
 )
 
-
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()