Update lang_graph.py

lang_graph.py

@@ -1,84 +1,84 @@
-from langchain_core.messages import HumanMessage, SystemMessage,AIMessageChunk
-from langchain_core.runnables.config import RunnableConfig
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain_google_genai import GoogleGenerativeAIEmbeddings
-from langchain_core.prompts import ChatPromptTemplate
-from langgraph.checkpoint.memory import MemorySaver
-from langgraph.graph import START, MessagesState, StateGraph
-from langsmith import traceable
-import chainlit as cl
-
-from dotenv import load_dotenv
-
-load_dotenv()
-
-workflow = StateGraph(state_schema=MessagesState)
-
-
-#print(os.environ.get("GOOGLE_API_KEY"))
-model = ChatGoogleGenerativeAI(model="gemini-2.5-pro", temperature=0.5)
-with open("sys_prompt.txt", "r",encoding="utf-8") as f:
-    sys_prompt=f.read()
-    ChatPromptTemplate.from_messages([SystemMessage(content=sys_prompt) ])
-
-#model = ChatOpenAI(model="gpt-4o-mini", temperature=0)
-
-
-def call_model(state: MessagesState):
-    response = model.invoke(state["messages"])
-    return {"messages": response}
-
-
-workflow.add_edge(START, "model")
-workflow.add_node("model", call_model)
-
-memory = MemorySaver()
-
-app = workflow.compile(checkpointer=memory)
-
-
-@cl.password_auth_callback
-def auth_callback(username: str, password: str):
-    # Fetch the user matching username from your database
-    # and compare the hashed password with the value stored in the database
-    if (username, password) == ("admin", "admin"):
-        return cl.User(
-            identifier="admin", metadata={"role": "admin", "provider": "credentials"}
-        )
-    else:
-        return None
-
-
-# @cl.on_chat_resume
-# async def on_chat_resume(thread):
-#     pass
-
-
-@cl.on_message
-async def main(message: cl.Message):
-
-    if message.elements:
-        for file in message.elements:
-            if file.mime not in ["image/png", "image/jpeg" , "document/pgf"]:
-                await cl.ErrorMessage(content="Unsupported file type").send()
-
-    answer = cl.Message(content="")
-    await answer.send()
-
-    config: RunnableConfig = {
-        "configurable": {"thread_id": cl.context.session.thread_id}
-    }
-
-
-    for msg, _ in app.stream(
-        {"messages": [HumanMessage(content=message.content)]},
-        config,
-        stream_mode="messages",
-    ):
-        if isinstance(msg, AIMessageChunk):
-            answer.content += msg.content  # type: ignore
-            await answer.update()
-
-@cl.on_audio_chunk
-async def on_audio_chunk(chunk: cl.InputAudioChunk):
+from langchain_core.messages import HumanMessage, SystemMessage,AIMessageChunk
+from langchain_core.runnables.config import RunnableConfig
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+from langchain_core.prompts import ChatPromptTemplate
+from langgraph.checkpoint.memory import MemorySaver
+from langgraph.graph import START, MessagesState, StateGraph
+from langsmith import traceable
+import chainlit as cl
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+workflow = StateGraph(state_schema=MessagesState)
+
+
+#print(os.environ.get("GOOGLE_API_KEY"))
+model = ChatGoogleGenerativeAI(model="gemini-2.5-pro", temperature=0.5)
+with open("sys_prompt.txt", "r",encoding="utf-8") as f:
+    sys_prompt=f.read()
+    ChatPromptTemplate.from_messages([SystemMessage(content=sys_prompt) ])
+
+#model = ChatOpenAI(model="gpt-4o-mini", temperature=0)
+
+
+def call_model(state: MessagesState):
+    response = model.invoke(state["messages"])
+    return {"messages": response}
+
+
+workflow.add_edge(START, "model")
+workflow.add_node("model", call_model)
+
+memory = MemorySaver()
+
+app = workflow.compile(checkpointer=memory)
+
+
+# @cl.password_auth_callback
+# def auth_callback(username: str, password: str):
+#     # Fetch the user matching username from your database
+#     # and compare the hashed password with the value stored in the database
+#     if (username, password) == ("admin", "admin"):
+#         return cl.User(
+#             identifier="admin", metadata={"role": "admin", "provider": "credentials"}
+#         )
+#     else:
+#         return None
+
+
+# @cl.on_chat_resume
+# async def on_chat_resume(thread):
+#     pass
+
+
+@cl.on_message
+async def main(message: cl.Message):
+
+    if message.elements:
+        for file in message.elements:
+            if file.mime not in ["image/png", "image/jpeg" , "document/pgf"]:
+                await cl.ErrorMessage(content="Unsupported file type").send()
+
+    answer = cl.Message(content="")
+    await answer.send()
+
+    config: RunnableConfig = {
+        "configurable": {"thread_id": cl.context.session.thread_id}
+    }
+
+
+    for msg, _ in app.stream(
+        {"messages": [HumanMessage(content=message.content)]},
+        config,
+        stream_mode="messages",
+    ):
+        if isinstance(msg, AIMessageChunk):
+            answer.content += msg.content  # type: ignore
+            await answer.update()
+
+@cl.on_audio_chunk
+async def on_audio_chunk(chunk: cl.InputAudioChunk):
     return {"audio": chunk}