Create app.py

app.py

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+import streamlit as st
+from gradio_client import Client
+from time import sleep
+# Constants
+TITLE = "Mistrial 7B Chatbot"
+DESCRIPTION = """
+This Space demonstrates model [Mistrial-7b-]
+"""
+
+# Initialize client
+
+
+with st.sidebar:
+    # system_promptSide = st.text_input("Optional system prompt:")
+    temperatureSide = st.slider("Temperature", min_value=0.0, max_value=1.0, value=0.9, step=0.05)
+    max_new_tokensSide = st.slider("Max new tokens", 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("TheBloke/Mistral-7B-Instruct-v0.1-GGUF", model_file="mistral-7b-instruct-v0.1.Q5_K_S.gguf", model_type="mistral", gpu_layers=0)
+ins = '''[INST] <<SYS>>
+You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, 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.
+<</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):
+    with st.status("Starting client"):
+        sleep(2)
+        st.write("Requesting client")
+    with st.status("Requesting LLama-2"):
+        st.write("Requesting API")
+         global conversation_history
+        # 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("Ask Mistril-7b anything..."):
+    # 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})