Update app.py

app.py

@@ -1,43 +1,144 @@
+# import streamlit as st
+# import random
+# import time
+# from mistralai.client import MistralClient
+# from mistralai.models.chat_completion import ChatMessage
+
+# api_key = st.secrets["MISTRAL_API_KEY"]
+# model = "mistral-large-latest"
+
+# client = MistralClient(api_key=api_key)
+
+# chat_response = client.chat(
+#     model=model,
+#     messages=[ChatMessage(role="user", content="What is the best French cheese?")]
+# )
+
+
+
+# # Streamed response emulator
+# def response_generator():
+#     response = client.chat(
+#     model=model,
+#     messages=[ChatMessage(role="user", content="What is the best French cheese?")]
+# )
+
+
+# st.title("Personality test")
+
+# # 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"])
+
+# # Accept user input
+# if prompt := st.chat_input("What is up?"):
+#     # Add user message to chat history
+#     st.session_state.messages.append({"role": "user", "content": prompt})
+#     # Display user message in chat message container
+#     with st.chat_message("user"):
+#         st.markdown(prompt)
+
+#     # Display assistant response in chat message container
+#     with st.chat_message("assistant"):
+#         response = st.write_stream(response_generator())
+#     # Add assistant response to chat history
+#     st.session_state.messages.append({"role": "assistant", "content": response})
+
+
+
+from mistralai.client import MistralClient
+from mistralai.models.chat_completion import ChatMessage
 import streamlit as st
-import random
-import time
+import json
+import faiss
+import numpy as np
 
+model = "open-mixtral-8x7b"
+mistral_api_key = st.secrets["MISTRAL_API_KEY"]
+client = MistralClient(api_key=mistral_api_key)
 
-# Streamed response emulator
-def response_generator():
-    response = random.choice(
-        [
-            "Hello there! How can I assist you today?",
-            "Hi, human! Is there anything I can help you with?",
-            "Do you need help?",
-        ]
-    )
-    for word in response.split():
-        yield word + " "
-        time.sleep(0.05)
+st.title("Assistant ChatBot catalogue 2024")
 
+def load_json(rep:str):
+    f = open(rep, encoding='UTF-8')
+    return json.load(f)
 
-st.title("Simple chat")
+def split_chunk(data, chunk_size):
+    data_str = [json.dumps(entry) for entry in data]
+    chunk_size = chunk_size
+    chunks = [data_str[i:i + chunk_size] for i in range(0, len(data_str), chunk_size)]
+    print(f"Nb. chunks = {len(chunks)}")
+    return chunks
+    
+def get_text_embedding(input):
+    embeddings_batch_response = client.embeddings(
+          model='mistral-embed',
+          input=input
+      )
+    return embeddings_batch_response.data[0].embedding
+
+def load_vector_db(text_embedded):
+    d = text_embedded.shape[1]
+    index = faiss.IndexFlatL2(d)
+    index.add(text_embedded)
+    return index
+
+def find_similar_chunk(index, question_embeddings, chunks):
+    D, I = index.search(question_embeddings, k=2) # distance, index
+    return [chunks[i] for i in I.tolist()[0]]
+
+def prompt_chat(retrieved_chunk, question):
+    return f"""
+    Les informations contextuelles sont les suivantes.
+    ---------------------
+    {retrieved_chunk}
+    ---------------------
+    Compte tenu des informations contextuelles et sans connaissances préalables,
+    réponds en français à la question suivante de manière concise.
+    Utilise des listes pour plus de lisibilité. 
+    Question: {question}
+    Réponse:
+    """
+
+# Chargement des données
+data = load_json('catalogue_2024.json')
+chunks = split_chunk(data, 3)
+text_embeddings = np.load("catalogue_embeddings.npy")
+index = load_vector_db(text_embeddings)
 
-# Initialize chat history
 if "messages" not in st.session_state:
-    st.session_state.messages = []
+    st.session_state["messages"] = [{"role": "assistant", "content": "Comment puis-je vous aider?"}]
+    st.session_state["History"] = []
+    st.session_state.History.append(ChatMessage(role="assitant", content="Comment puis-je vous aider?"))
+
+for msg in st.session_state.messages:
+    st.chat_message(msg["role"]).write(msg["content"])
 
-# 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"])
+if prompt := st.chat_input():
+    question_embeddings = np.array([get_text_embedding(prompt)])
+    retrieved_chunk = find_similar_chunk(index, question_embeddings, chunks)
+    p = prompt_chat(retrieved_chunk=retrieved_chunk, question=prompt)
 
-# Accept user input
-if prompt := st.chat_input("What is up?"):
-    # Add user message to chat history
     st.session_state.messages.append({"role": "user", "content": prompt})
-    # Display user message in chat message container
-    with st.chat_message("user"):
-        st.markdown(prompt)
+    st.session_state.History.append(ChatMessage(role="user", content=p))
+    st.chat_message("user").write(prompt)
 
-    # Display assistant response in chat message container
     with st.chat_message("assistant"):
-        response = st.write_stream(response_generator())
-    # Add assistant response to chat history
-    st.session_state.messages.append({"role": "assistant", "content": response})
+        message_placeholder = st.empty()
+        full_response = ""
+        for response in client.chat_stream(
+            model=model,
+            messages=st.session_state.History[1:]
+        ):
+            full_response += (response.choices[0].delta.content or "")
+            message_placeholder.markdown(full_response + "|")
+        
+        message_placeholder.markdown(full_response)
+        
+        st.session_state.History.append(ChatMessage(role="assistant", content=full_response))
+        st.session_state.messages.append({"role": "assistant", "content": full_response})