Update app.py

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 
-MODEL_NAME = "microsoft/DialoGPT-small"  # small, free, OK on CPU [web:103]
+MODEL_NAME = "microsoft/DialoGPT-small"  # small, CPU-friendly
 
 tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
 model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
@@ -39,53 +39,59 @@ Rules:
 
 
 def build_prompt(message, history):
-    """Turn chat history into a single text prompt for DialoGPT."""
-    lines = [f"System: {SHINCHAN_SYSTEM_PROMPT}"]
-    for user_msg, bot_msg in history:
-        if user_msg:
-            lines.append(f"User: {user_msg}")
-        if bot_msg:
-            lines.append(f"Shinchan: {bot_msg}")
-    lines.append(f"User: {message}")
-    lines.append("Shinchan:")
-    return "\n".join(lines)
+  """
+  Gradio ChatInterface (type='messages') passes history as a list of
+  dicts like: {'role': 'user'|'assistant', 'content': '...'}.[web:120]
+  Turn that into a plain-text prompt for DialoGPT.
+  """
+  lines = [f"System: {SHINCHAN_SYSTEM_PROMPT}"]
+  for turn in history:
+    role = turn.get("role")
+    content = turn.get("content", "")
+    if role == "user":
+      lines.append(f"User: {content}")
+    elif role == "assistant":
+      lines.append(f"Shinchan: {content}")
+  lines.append(f"User: {message}")
+  lines.append("Shinchan:")
+  return "\n".join(lines)
 
 
 def respond(message, history):
-    if not history:
-        history = []
-
-    prompt = build_prompt(message, history)
-    inputs = tokenizer(prompt, return_tensors="pt")
-
-    # keep it small/light for CPU Spaces
-    with torch.no_grad():
-        output_ids = model.generate(
-          **inputs,
-          max_new_tokens=80,
-          pad_token_id=tokenizer.eos_token_id,
-          do_sample=True,
-          top_p=0.9,
-          temperature=0.9,
-        )
-
-    full_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
-    # get only the last Shinchan part after final "Shinchan:"
-    if "Shinchan:" in full_text:
-        reply = full_text.split("Shinchan:")[-1].strip()
-    else:
-        reply = full_text.strip()
-
-    # add reply to history; Gradio ChatInterface expects (history, response)
-    history = history + [(message, reply)]
-    return reply, history
+  """
+  ChatInterface expects: fn(message:str, history:list[dict]) -> str or dict or list.[web:120]
+  Do NOT return (reply, history) here – that caused your 'tuple has no attribute get' error.
+  """
+  prompt = build_prompt(message, history)
+  inputs = tokenizer(prompt, return_tensors="pt")
+
+  with torch.no_grad():
+    output_ids = model.generate(
+      **inputs,
+      max_new_tokens=80,
+      pad_token_id=tokenizer.eos_token_id,
+      do_sample=True,
+      top_p=0.9,
+      temperature=0.9,
+    )
+
+  full_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+  if "Shinchan:" in full_text:
+    reply = full_text.split("Shinchan:")[-1].strip()
+  else:
+    reply = full_text.strip()
+
+  # IMPORTANT: return just the reply string
+  return reply
 
 
 demo = gr.ChatInterface(
-    respond,
-    title="Shinchan for Ruru",
-    description="Private Shinchan-style chat for Ruru.",
+  fn=respond,
+  type="messages",  # explicit; default since Gradio 5 but makes intent clear[web:120]
+  title="Shinchan for Ruru",
+  description="Private Shinchan-style chat for Ruru.",
 )
 
 if __name__ == "__main__":
-    demo.launch()
+  demo.launch()