Update src/processor/message_processor.py

src/processor/message_processor.py

@@ -1,20 +1,97 @@
-# SPDX-FileCopyrightText: Hadad <[email protected]>
 # SPDX-License-Identifier: Apache-2.0
-#
+"""
+Universal message processor that is compatible with Gradio ChatInterface passing:
+- (message: str, history: list[tuple[str, str]] or list[dict]])
+- (messages: list[dict]) when type="messages"
+It normalizes into OpenAI/Gemini-style [{"role":"user"|"assistant","content":"..."}].
+Then it calls the Gemini provider to generate a reply.
+"""
 
-from typing import List, Dict
-from config import INSTRUCTIONS_START, INSTRUCTIONS_END
+from typing import List, Dict, Tuple, Any, Optional
+from config import MODEL, INSTRUCTIONS_START, INSTRUCTIONS_END, MAX_TOKENS  # MODEL 來自 config
 from src.providers.gemini import GeminiProvider
 
-def process_user_request(messages: List[Dict[str, str]]) -> str:
+# --------- helpers ---------
+def _tuple_history_to_messages(history: List[Tuple[Optional[str], Optional[str]]]) -> List[Dict[str, str]]:
     """
-    主入口：處理使用者輸入，呼叫 Gemini API。
-    messages: [{"role": "user"/"assistant", "content": "..."}]
+    Convert ChatInterface tuple history [(user, assistant), ...] -> role/content messages.
     """
-    system_prompt = INSTRUCTIONS_START + "\n" + INSTRUCTIONS_END
+    msgs: List[Dict[str, str]] = []
+    for ua in history or []:
+        if not isinstance(ua, (list, tuple)) or len(ua) != 2:
+            # already normalized or unexpected; skip
+            continue
+        user_msg, assistant_msg = ua
+        if user_msg:
+            msgs.append({"role": "user", "content": str(user_msg)})
+        if assistant_msg:
+            msgs.append({"role": "assistant", "content": str(assistant_msg)})
+    return msgs
+
+def _dict_history_to_messages(history: List[Dict[str, Any]]) -> List[Dict[str, str]]:
+    """
+    Pass-through for ChatInterface type='messages' history.
+    """
+    msgs: List[Dict[str, str]] = []
+    for m in history or []:
+        role = m.get("role")
+        content = m.get("content")
+        if role and content is not None:
+            msgs.append({"role": str(role), "content": str(content)})
+    return msgs
+
+def _normalize_inputs(*args: Any) -> List[Dict[str, str]]:
+    """
+    Accepts either:
+      - (messages: list[dict])        # type='messages'
+      - (message: str, history: list) # default type
+    Returns list[{"role", "content"}]
+    """
+    # Case A: single argument
+    if len(args) == 1:
+        payload = args[0]
+        # Already messages list (type="messages")
+        if isinstance(payload, list) and (not payload or isinstance(payload[0], dict)):
+            return _dict_history_to_messages(payload)
+        # Single string -> single-turn chat
+        if isinstance(payload, str):
+            return [{"role": "user", "content": payload}]
+        # Fallback
+        return [{"role": "user", "content": str(payload)}]
+
+    # Case B: two arguments (message, history)
+    if len(args) >= 2:
+        message, history = args[0], args[1]
+        msgs: List[Dict[str, str]] = []
+        if isinstance(history, list):
+            if history and isinstance(history[0], dict):
+                msgs.extend(_dict_history_to_messages(history))
+            else:
+                msgs.extend(_tuple_history_to_messages(history))
+        # append current user message if exists
+        if message is not None and message != "":
+            msgs.append({"role": "user", "content": str(message)})
+        return msgs
+
+    # Fallback (no args)
+    return [{"role": "user", "content": ""}]
+
+# --------- main entry called by Gradio ChatInterface (see app.py)---------
+def process_user_request(*args: Any) -> str:
+    """
+    Gradio will call with either (message, history) or (messages).
+    We normalize to messages and call Gemini to generate a reply.
+    """
+    messages = _normalize_inputs(*args)
+
+    # System prompt：沿用 config 的強制工具/交叉驗證規則
+    system_prompt = (INSTRUCTIONS_START or "") + "\n" + (INSTRUCTIONS_END or "")
+
     reply = GeminiProvider.generate(
-        model_name="gemini-1.5-flash",  # 或從 config.MODEL 載入
-        messages=messages,
-        system_prompt=system_prompt,
+        model_name=MODEL,              # e.g., "gemini-1.5-flash"
+        messages=messages,             # normalized messages
+        system_prompt=system_prompt,   # prepend system rules
+        max_tokens=MAX_TOKENS,         # internal output cap
+        temperature=0.7,
     )
     return reply