Upload app_interface_working_1.py

app_interface_working_1.py

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+import streamlit as st
+from llama_cpp import Llama
+
+st.set_page_config(page_title="Chat with AI", page_icon="🤖")
+
+# Custom CSS for better styling
+st.markdown("""
+<style>
+.stTextInput > div > div > input {
+    background-color: #f0f2f6;
+}
+.chat-message {
+    padding: 1.5rem; border-radius: 0.5rem; margin-bottom: 1rem; display: flex
+}
+.chat-message.user {
+    background-color: #2b313e
+}
+.chat-message.bot {
+    background-color: #475063
+}
+.chat-message .avatar {
+  width: 20%;
+}
+.chat-message .avatar img {
+  max-width: 78px;
+  max-height: 78px;
+  border-radius: 50%;
+  object-fit: cover;
+}
+.chat-message .message {
+  width: 80%;
+  padding: 0 1.5rem;
+  color: #fff;
+}
+</style>
+""", unsafe_allow_html=True)
+
+@st.cache_resource
+def load_model():
+    return Llama.from_pretrained(
+        repo_id="Mykes/med_phi3-mini-4k-GGUF",
+        filename="*Q4_K_M.gguf",
+        verbose=False,
+        n_ctx=512,
+        n_batch=256,
+        n_threads=4
+    )
+
+llm = load_model()
+
+def format_context(messages):
+    context = ""
+    for message in messages:
+        if message["role"] == "user":
+            context += f"Human: {message['content']}\n"
+        else:
+            context += f"Assistant: {message['content']}\n"
+    return context
+
+# Initialize chat history
+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"]):
+        st.markdown(message["content"])
+
+# React to user input
+if prompt := st.chat_input("What is your question?"):
+    # Display user message in chat message container
+    st.chat_message("user").markdown(prompt)
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+    # Format the context with the last 5 messages
+    context = format_context(st.session_state.messages[-5:])
+    
+    # Prepare the model input
+    model_input = f"{context}Human: {prompt}\nAssistant:"
+    
+    # Display assistant response in chat message container
+    with st.chat_message("assistant"):
+        message_placeholder = st.empty()
+        full_response = ""
+        
+        for token in llm(
+            model_input,
+            max_tokens=None,
+            stop=["Human:", "<end_of_turn>"],
+            echo=True,
+            stream=True
+        ):
+            full_response += token['choices'][0]['text']
+            message_placeholder.markdown(full_response + "▌")
+        
+        # Remove the initial context and prompt from the response
+        assistant_response = full_response.split("Assistant:")[-1].strip()
+        message_placeholder.markdown(assistant_response)
+    
+    # Add assistant response to chat history
+    st.session_state.messages.append({"role": "assistant", "content": assistant_response})
+
+st.sidebar.title("Chat with AI")
+st.sidebar.markdown("This is a simple chat interface using Streamlit and an AI model.")