add title

chat_app.py

@@ -1,9 +1,10 @@
 import time
 import os
 import gradio as gr
-from typing import List
+from typing import List, Optional
 
 import langchain_core.callbacks
+import markdown_it.cli.parse
 from langchain_huggingface import HuggingFaceEndpoint
 
 from langchain.schema import BaseMessage
@@ -31,6 +32,7 @@ class InMemoryHistory(BaseChatMessageHistory, BaseModel):
 
 # In-memory storage for session history
 store = {}
+bot_llm:Optional[RunnableWithMessageHistory] = None
 
 def get_session_history(
     user_id: str, conversation_id: str
@@ -39,65 +41,71 @@ def get_session_history(
         store[(user_id, conversation_id)] = InMemoryHistory()
     return store[(user_id, conversation_id)]
 
-prompt = ChatPromptTemplate.from_messages([
-    ("system", "[INST] You're an assistant who's good at everything"),
-    MessagesPlaceholder(variable_name="history"),
-    ("human", "{question} [/INST]"),
-])
-
-model_id="mistralai/Mistral-7B-Instruct-v0.3"
-callbacks = [langchain_core.callbacks.StreamingStdOutCallbackHandler()]
-llm = HuggingFaceEndpoint(
-    repo_id=model_id,
-    max_new_tokens=512,
-    temperature=0.1,
-    repetition_penalty=1.03,
-    callbacks=callbacks,
-    streaming=True,
-    huggingfacehub_api_token=os.getenv('HF_TOKEN'),
-)
 
-chain = prompt | llm
-
-with_message_history = RunnableWithMessageHistory(
-    chain,
-    get_session_history=get_session_history,
-    input_messages_key="question",
-    history_messages_key="history",
-    history_factory_config=[
-        ConfigurableFieldSpec(
-            id="user_id",
-            annotation=str,
-            name="User ID",
-            description="Unique identifier for the user.",
-            default="",
-            is_shared=True,
-        ),
-        ConfigurableFieldSpec(
-            id="conversation_id",
-            annotation=str,
-            name="Conversation ID",
-            description="Unique identifier for the conversation.",
-            default="",
-            is_shared=True,
-        ),
-    ],
-)
+def init_llm(k, p, t):
+    global bot_llm
+    prompt = ChatPromptTemplate.from_messages([
+        ("system", "[INST] You're an assistant who's good at everything"),
+        MessagesPlaceholder(variable_name="history"),
+        ("human", "{question} [/INST]"),
+    ])
+
+    model_id="mistralai/Mistral-7B-Instruct-v0.3"
+    callbacks = [langchain_core.callbacks.StreamingStdOutCallbackHandler()]
+
+    llm = HuggingFaceEndpoint(
+        repo_id=model_id,
+        max_new_tokens=4096,
+        temperature=t,
+        top_p=p,
+        top_k=k,
+        repetition_penalty=1.03,
+        callbacks=callbacks,
+        streaming=True,
+        huggingfacehub_api_token=os.getenv('HF_TOKEN'),
+    )
+
+    chain = prompt | llm
+    with_message_history = RunnableWithMessageHistory(
+        chain,
+        get_session_history=get_session_history,
+        input_messages_key="question",
+        history_messages_key="history",
+        history_factory_config=[
+            ConfigurableFieldSpec(
+                id="user_id",
+                annotation=str,
+                name="User ID",
+                description="Unique identifier for the user.",
+                default="",
+                is_shared=True,
+            ),
+            ConfigurableFieldSpec(
+                id="conversation_id",
+                annotation=str,
+                name="Conversation ID",
+                description="Unique identifier for the conversation.",
+                default="",
+                is_shared=True,
+            ),
+        ],
+    )
+    bot_llm = with_message_history
+    return gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True), gr.update(open=False)
 
 with gr.Blocks() as demo:
+    gr.HTML("<center><h1>Chat with a Smart Assistant</h1></center>")
     chatbot = gr.Chatbot(type="messages")
-    msg = gr.Textbox()
-    stop = gr.Button("Stop")
-    clear = gr.Button("Clear")
+    msg = gr.Textbox(placeholder="Enter text and press enter", interactive=False)
+    stop = gr.Button("Stop", interactive=False)
+    clear = gr.Button("Clear",interactive=False)
 
     def user(user_message, history: list):
         return "", history + [{"role": "user", "content": user_message}]
 
     def bot(history: list):
-
         question = history[-1]['content']
-
-        answer = with_message_history.stream(
+        answer = bot_llm.stream(
             {"ability": "everything", "question": question},
             config={"configurable": {"user_id": "123", "conversation_id": "1"}}
         )
@@ -106,11 +114,18 @@ with gr.Blocks() as demo:
             history[-1]['content'] += character
             time.sleep(0.05)
             yield history
-        # for item in answer:
-        #     for character in item.content:
-        #         history[-1]['content'] += character
-        #         time.sleep(0.05)
-        #         yield history
+
+    with gr.Sidebar() as s:
+        gr.HTML("<h1>Model Configuration<h1>")
+        k = gr.Slider(0.0, 100.0, label="top_k", value=50, interactive=True,
+                  info="Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40)")
+        p = gr.Slider(0.0, 1.0, label="top_p", value=0.9, interactive=True,
+                  info=" Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)")
+        t = gr.Slider(0.0, 1.0, label="temperature", value=0.4, interactive=True,
+                  info="The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8)")
+
+        bnt1 = gr.Button("Confirm")
+        bnt1.click(init_llm, inputs=[k, p, t], outputs=[msg, stop, clear, s])
 
     submit_event = msg.submit(user, [msg, chatbot], [msg, chatbot], queue=True).then(
         bot, chatbot, chatbot