Update app.py

app.py

@@ -7,269 +7,6 @@ from db import SessionLocal, User, Chat, Message
 
 claude = ClaudeAnswerer()
 
-# app.py
-import time
-import chainlit as cl
-
-from srd_engine_v2 import SmartKnowledgeBase, ClaudeAnswerer
-from db import SessionLocal, User, Chat, Message
-
-claude = ClaudeAnswerer()
-
-
-@cl.on_chat_start
-async def start():
-    session = cl.user_session
-    db = SessionLocal()
-    try:
-        # -----------------------
-        # USER IDENTIFICATION
-        # -----------------------
-        if not session.get("user_id"):
-            user = User()
-            db.add(user)
-            db.commit()
-            session.set("user_id", user.id)
-
-        user_id = session.get("user_id")
-
-        # -----------------------
-        # CHAT SELECTION
-        # -----------------------
-        chats = db.query(Chat).filter(Chat.user_id == user_id).all()
-
-        actions = [cl.Action(name="new_chat", payload={}, label="➕ New Project Chat")]
-        for c in chats[-5:]:
-            actions.append(
-                cl.Action(
-                    name="resume_chat",
-                    payload={"chat_id": c.id},
-                    label=f"📂 {c.project_name}"
-                )
-            )
-
-        res = await cl.AskActionMessage(
-            content="Choose a chat or start a new one:",
-            actions=actions
-        ).send()
-
-        if not res:
-            return
-
-        # -----------------------
-        # NEW CHAT
-        # -----------------------
-        if res["name"] == "new_chat":
-            project_res = await cl.AskUserMessage(
-                content="Enter **Project Name**:",
-                timeout=300
-            ).send()
-            if not project_res:
-                return
-            project_name = project_res["output"]
-
-            learn_res = await cl.AskActionMessage(
-                content="Allow this chat to be saved/learned for improving the bot?",
-                actions=[
-                    cl.Action(name="learn_yes", payload={"v": True}, label="✅ Yes (Enable Learning)"),
-                    cl.Action(name="learn_no", payload={"v": False}, label="❌ No (Do Not Learn)"),
-                ],
-            ).send()
-            learning_enabled = bool(learn_res["payload"]["v"]) if learn_res else True
-
-            chat = Chat(user_id=user_id, project_name=project_name, learning_enabled=learning_enabled)
-            db.add(chat)
-            db.commit()
-
-            session.set("chat_id", chat.id)
-            session.set("learning_enabled", chat.learning_enabled)
-
-            engine = SmartKnowledgeBase(chroma_dir="chroma_global_db")
-            engine.set_current_project(project_name)
-            engine.set_current_chat(chat.id)     # ✅ NEW
-            engine.set_current_user(user_id)     # ✅ NEW
-            session.set("engine", engine)
-
-            await run_ingestion(engine)
-
-        # -----------------------
-        # RESUME CHAT
-        # -----------------------
-        else:
-            chat_id = res["payload"]["chat_id"]
-            chat = db.query(Chat).get(chat_id)
-            if not chat:
-                await cl.Message(content="⚠️ Chat not found.").send()
-                return
-
-            session.set("chat_id", chat.id)
-            session.set("learning_enabled", chat.learning_enabled)
-
-            engine = SmartKnowledgeBase(chroma_dir="chroma_global_db")
-            engine.set_current_project(chat.project_name)
-            engine.set_current_chat(chat.id)     # ✅ NEW
-            engine.set_current_user(user_id)     # ✅ NEW
-            session.set("engine", engine)
-
-            # Restore history
-            messages = (
-                db.query(Message)
-                .filter(Message.chat_id == chat_id)
-                .order_by(Message.created_at)
-                .all()
-            )
-            for m in messages:
-                await cl.Message(content=m.content, author=m.role).send()
-
-    finally:
-        db.close()
-
-
-async def run_ingestion(engine: SmartKnowledgeBase):
-    files = await cl.AskFileMessage(
-        content="Upload the **SRD PDF**:",
-        accept=["application/pdf"],
-        max_size_mb=50,
-        timeout=600
-    ).send()
-    if not files:
-        return
-
-    srd_file_path = files[0].path
-
-    res = await cl.AskActionMessage(
-        content="Select Diagram Vision Mode:",
-        actions=[
-            cl.Action(name="qwen", payload={"v": "qwen"}, label="Qwen"),
-            cl.Action(name="claude", payload={"v": "claude"}, label="Claude"),
-            cl.Action(name="both", payload={"v": "both"}, label="Both"),
-            cl.Action(name="none", payload={"v": "none"}, label="None"),
-        ]
-    ).send()
-
-    mode = res["payload"]["v"] if res else "none"
-    use_qwen = mode in ("qwen", "both")
-    use_claude = mode in ("claude", "both")
-
-    status = cl.Message(content="🚀 Starting ingestion...")
-    await status.send()
-
-    await cl.make_async(engine.process_document_step)(
-        srd_file_path, "pdf_text", "SRD Main", False, False
-    )
-
-    status.content += "\n✅ SRD indexed"
-    await status.update()
-
-    while True:
-        add = await cl.AskActionMessage(
-            content="Add a diagram?",
-            actions=[
-                cl.Action(name="yes", payload={}, label="➕ Add"),
-                cl.Action(name="done", payload={}, label="Done"),
-            ]
-        ).send()
-
-        if not add or add["name"] == "done":
-            break
-
-        title = await cl.AskUserMessage(content="Diagram title:", timeout=300).send()
-        if not title:
-            break
-
-        file = await cl.AskFileMessage(
-            content="Upload diagram:",
-            accept=["image/png", "image/jpeg", "application/pdf"],
-            max_size_mb=20,
-            timeout=600
-        ).send()
-
-        if not file:
-            break
-
-        await cl.make_async(engine.process_document_step)(
-            file[0].path, "diagram", title["output"], use_qwen, use_claude
-        )
-
-        status.content += f"\n🎨 Diagram '{title['output']}' indexed"
-        await status.update()
-
-    status.content += "\n🎉 Ingestion complete. Ask questions!"
-    await status.update()
-
-
-@cl.on_message
-async def main(message: cl.Message):
-    session = cl.user_session
-    db = SessionLocal()
-    try:
-        engine: SmartKnowledgeBase = session.get("engine")
-        chat_id = session.get("chat_id")
-
-        if not engine or not chat_id:
-            await cl.Message(content="⚠️ Session error. Please refresh.").send()
-            return
-
-        # Save user msg
-        db.add(Message(chat_id=chat_id, role="user", content=message.content))
-        db.commit()
-
-        # Answer
-        response = await cl.make_async(engine.generate_smart_response)(message.content, claude)
-
-        # Save assistant msg
-        db.add(Message(chat_id=chat_id, role="assistant", content=response))
-        db.commit()
-
-        await cl.Message(content=response).send()
-
-        # Feedback
-        await cl.Message(
-            content="",
-            actions=[
-                cl.Action(
-                    name="correct",
-                    payload={"original": message.content},
-                    label="🔧 Correct This"
-                )
-            ]
-        ).send()
-
-    finally:
-        db.close()
-
-
-@cl.action_callback("correct")
-async def on_correct(action):
-    session = cl.user_session
-    db = SessionLocal()
-    try:
-        engine: SmartKnowledgeBase = session.get("engine")
-        chat_id = session.get("chat_id")
-        learning_enabled = bool(session.get("learning_enabled", True))
-
-        await action.remove()
-
-        res = await cl.AskUserMessage(
-            content="Paste the correct information:",
-            timeout=600
-        ).send()
-        if not res:
-            return
-
-        # Always store correction text in DB (audit trail)
-        db.add(Message(chat_id=chat_id, role="user_feedback", content=res["output"]))
-        db.commit()
-
-        # Learn only if allowed
-        if learning_enabled:
-            engine.learn_from_interaction(action.payload["original"], res["output"])
-            await cl.Message(content="✅ Correction saved and learned.").send()
-        else:
-            await cl.Message(content="✅ Correction saved (learning disabled for this chat).").send()
-
-    finally:
-        db.close()
 
 @cl.on_chat_start
 async def start():