Steven is woest

Dockerfile

@@ -0,0 +1,17 @@
+# 
+FROM python:3.9
+
+# 
+WORKDIR /code
+
+# 
+COPY ./requirements.txt /code/requirements.txt
+
+# 
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# 
+COPY ./app /code/app
+
+# 
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "80"]

README.md

@@ -3,7 +3,7 @@ title: Team3
 emoji: 🦀
 colorFrom: yellow
 colorTo: purple
-sdk: streamlit
+sdk: docker
 sdk_version: 1.33.0
 app_file: app.py
 pinned: false

app.py

@@ -1,153 +0,0 @@
-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})
-
-    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})
-
-
-
-# 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})

requirements.txt

@@ -1,3 +1,3 @@
 # Add the other requirements here
-mistralai
-faiss-gpu
+fastapi
+elevenlabs