streamlit app

app.py

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+import streamlit as st
+from rag import RAG
+
+llm_model_path = '/Users/peportier/llm/a/a/zephyr-7b-beta.Q5_K_M.gguf'
+embed_model_name = 'intfloat/multilingual-e5-large'
+collection_name = 'cera'
+chromadb_path = './chromadb'
+
+rag = RAG(llm_model_path, embed_model_name, collection_name, chromadb_path)
+
+st.title("CERA Chat")
+
+if st.sidebar.button("Nouvelle conversation"):
+    st.session_state.messages = []
+    rag.reset_history()
+
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+if prompt := st.chat_input("Comment puis-je vous aider ?"):
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.markdown(prompt)
+    
+    with st.chat_message("assistant"):
+        message_placeholder = st.empty()
+        full_response = ""
+        for response in rag.chat(prompt):
+            full_response += response
+            message_placeholder.markdown(full_response + "▌")
+        message_placeholder.markdown(full_response)
+    st.session_state.messages.append({"role": "assistant", "content": full_response})