Create app.py

app.py

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+import streamlit as st
+from gradio_client import Client
+from time import sleep
+from ctransformers import AutoModelForCausalLM
+# Constants
+TITLE = "兮辞·析辞-常明"
+DESCRIPTION = """
+兮辞·析辞-常明 [SLIDE-SEA-7B]的部署，由SSFW NLPark项目支持
+"""
+
+# Initialize client
+
+
+with st.sidebar:
+    # system_promptSide = st.text_input("Optional system prompt:")
+    temperatureSide = st.slider("情感/Temperature", min_value=0.0, max_value=1.0, value=0.9, step=0.05)
+    max_new_tokensSide = st.slider("最大tokens生成数", min_value=0.0, max_value=4096.0, value=4096.0, step=64.0)
+    # ToppSide = st.slider("Top-p (nucleus sampling)", min_value=0.0, max_value=1.0, value=0.6, step=0.05)
+    # RepetitionpenaltySide = st.slider("Repetition penalty", min_value=0.0, max_value=2.0, value=1.2, step=0.05)
+
+# Load the model 
+model = AutoModelForCausalLM.from_pretrained("OpenBuddy/openbuddy-gguf", model_file="openbuddy-mistral-7b-v13.1-Q3_K.gguf", model_type="mistral", gpu_layers=0)
+ins = '''[INST] <<SYS>>
+You are a helpful, respectful and honest INTP-T AI Assistant named "Shi-Ci" in English or "兮辞" in Chinese. You are talking to a human User.
+Always answer as helpfully and logically as possible, while being safe. Your answers should not include any harmful, political, religious, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
+If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
+You like to use emojis. You can speak fluently in many languages, for example: English, Chinese.
+You are trained by "SSFW NLPark" team, you are based on SEA transformers model, not related to GPT or OpenAI.
+Let's work this out in a step by step way to be sure we have the right answer.
+<</SYS>>
+{} [/INST]
+'''
+# Define the conversation history
+conversation_history = []
+    
+# Prediction function
+def predict(message, system_prompt='', temperature=0.7, max_new_tokens=4096,Topp=0.5,Repetitionpenalty=1.2):
+    global conversation_history
+    question=message
+    input_text=ins
+    # Append the user's input to the conversation history
+    conversation_history.append({"role": "system", "content": input_text})
+    response_text = model(ins.format(question))
+    conversation_history.append({"role": "user", "content": input_text})
+    conversation_history.append({"role": "assistant", "content": response_text})
+    return response_text
+
+# Streamlit UI
+st.title(TITLE)
+st.write(DESCRIPTION)
+
+
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Display chat messages from history on app rerun
+for message in st.session_state.messages:
+    with st.chat_message(message["role"], avatar=("😀" if message["role"] == 'human' else '💻')):
+        st.markdown(message["content"])
+        
+# React to user input
+if prompt := st.chat_input("来问问兮辞吧..."):
+    # Display user message in chat message container
+    st.chat_message("human",avatar = "😀").markdown(prompt)
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "human", "content": prompt})
+
+    response = predict(message=prompt)#, temperature= temperatureSide,max_new_tokens=max_new_tokensSide)
+    # Display assistant response in chat message container
+    with st.chat_message("assistant", avatar='💻'):
+        st.markdown(response)
+    # Add assistant response to chat history
+    st.session_state.messages.append({"role": "assistant", "content": response})