little reset

app.py

@@ -3,46 +3,51 @@ from huggingface_hub import InferenceClient
 from datetime import datetime
 import os
 import uuid
-from huggingface_hub import HfApi
-
-api = HfApi()
 
 # ---- System Prompt ----
 with open("system_prompt.txt", "r") as f:
     SYSTEM_PROMPT = f.read()
 
-# ---- Constants ----
 MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
+
 DATASET_REPO = "frimelle/companion-chat-logs"
 HF_TOKEN = os.environ.get("HF_TOKEN")  # set in Space secrets
+
 client = InferenceClient(MODEL_NAME)
 
-# ---- Upload to Dataset ----
-def upload_chat_to_dataset(user_message, assistant_message, system_prompt):
-    row = {
-        "timestamp": datetime.now().isoformat(),
-        "session_id": str(uuid.uuid4()),
-        "user": user_message,
-        "assistant": assistant_message,
-        "system_prompt": system_prompt,
-    }
+# ---- Setup logging ----
+LOG_DIR = "chat_logs"
+os.makedirs(LOG_DIR, exist_ok=True)
+session_id = str(uuid.uuid4())
 
-    dataset = Dataset.from_dict({k: [v] for k, v in row.items()})
-    dataset.push_to_hub(DATASET_REPO, private=True, token=HF_TOKEN)
+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")
 
-# ---- Chat Function ----
-def respond(message, history, system_message, max_tokens, temperature, top_p):
+# ---- Respond Function with Logging ----
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+):
     messages = [{"role": "system", "content": system_message}]
 
-    for user_msg, bot_msg in history:
-        if user_msg:
-            messages.append({"role": "user", "content": user_msg})
-        if bot_msg:
-            messages.append({"role": "assistant", "content": bot_msg})
+    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 chunk in client.chat_completion(
         messages,
         max_tokens=max_tokens,
@@ -55,22 +60,20 @@ def respond(message, history, system_message, max_tokens, temperature, top_p):
             response += token
             yield response
 
-    # Log the final full message to the dataset
-    upload_chat_to_dataset(message, response, system_message)
+    # Save full message after stream ends
+    log_chat(session_id, message, response)
 
-# ---- Gradio UI ----
+# ---- Gradio Interface ----
 demo = gr.ChatInterface(
-    fn=respond,
+    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)"),
     #],
-    title="BoundrAI",
+    title="BoundrAI"
 )
 
-print(api.whoami(token=HF_TOKEN))
-
 if __name__ == "__main__":
     demo.launch()