Update app.py

app.py

@@ -1,132 +1,53 @@
-# 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?")]
-# )
-
+import streamlit as st
+import random
+import time
+from mistralai.client import MistralClient
+from mistralai.models.chat_completion import ChatMessage
 
-# st.title("Personality test")
+api_key = st.secrets["MISTRAL_API_KEY"]
+model = "mistral-large-latest"
 
-# # Initialize chat history
-# if "messages" not in st.session_state:
-#     st.session_state.messages = []
+client = MistralClient(api_key=api_key)
 
-# # 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"])
+chat_response = client.chat(
+    model=model,
+    messages=[ChatMessage(role="user", content="What is the best French cheese?")]
+)
 
-# # 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})
 
+# Streamed response emulator
+def response_generator():
+    response = client.chat(
+    model=model,
+    messages=[ChatMessage(role="user", content="What is the best French cheese?")]
+)
 
 
-from mistralai.client import MistralClient
-from mistralai.models.chat_completion import ChatMessage
-import streamlit as st
-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)
-
-st.title("Assistant ChatBot catalogue 2024")
-
-def load_json(rep:str):
-    f = open(rep, encoding='UTF-8')
-    return json.load(f)
-
-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)
+st.title("Personality test")
 
+# Initialize chat history
 if "messages" not in st.session_state:
-    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?"))
+    st.session_state.messages = []
 
-for msg in st.session_state.messages:
-    st.chat_message(msg["role"]).write(msg["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)
+# 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})
-    st.session_state.History.append(ChatMessage(role="user", content=p))
-    st.chat_message("user").write(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})
 
     with st.chat_message("assistant"):
         message_placeholder = st.empty()
@@ -141,4 +62,92 @@ if prompt := st.chat_input():
         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})
+        st.session_state.messages.append({"role": "assistant", "content": full_response})
+
+
+
+# from mistralai.client import MistralClient
+# from mistralai.models.chat_completion import ChatMessage
+# import streamlit as st
+# 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)
+
+# st.title("Assistant ChatBot")
+
+
+
+# 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
+
+# if "messages" not in st.session_state:
+#     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"])
+
+# 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)
+
+#     st.session_state.messages.append({"role": "user", "content": prompt})
+#     st.session_state.History.append(ChatMessage(role="user", content=p))
+#     st.chat_message("user").write(prompt)
+
+#     with st.chat_message("assistant"):
+#         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})