Spaces:
Sleeping
Sleeping
File size: 5,474 Bytes
b4e5268 1fe0c9e 8f64959 55a8b20 dd507bb 4f4aca6 b4e5268 1fe0c9e 397c421 6a7d03a 1fe0c9e 6a7d03a 47025c6 d5caf79 b4e5268 b956157 1fe0c9e b4e5268 47025c6 b4e5268 1fe0c9e b4e5268 47025c6 1fe0c9e b4e5268 0000cad 4f4aca6 47025c6 b4e5268 1fe0c9e b4e5268 47025c6 b4e5268 1fe0c9e b4e5268 47025c6 b4e5268 6812dc5 1fe0c9e 0b0e73b ab55f29 647afad ab55f29 1fe0c9e 47025c6 647afad e958527 2b8b939 5d2d937 2b8b939 1fe0c9e 2b8b939 1fe0c9e 598d787 ffdd294 417bcab ffdd294 417bcab 1fe0c9e e575254 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 |
import os
import csv
import streamlit as st
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_huggingface import HuggingFaceEndpoint
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain, RetrievalQA
from huggingface_hub import login
# Login to Hugging Face
login(token=st.secrets["HF_TOKEN"])
# Load FAISS index and ensure it only happens once
if 'db' not in st.session_state:
st.session_state.db = FAISS.load_local(
"faiss_index",
HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L12-v2'),
allow_dangerous_deserialization=True
)
# Use session state for retriever
retriever = st.session_state.db.as_retriever(
search_type="mmr",
search_kwargs={'k': 1}
)
# Define prompt template
prompt_template = """
### [INST]
Instruction: You are a Q&A assistant. Your goal is to answer questions as accurately as possible based on the instructions and context provided without using prior knowledge. You answer in FRENCH
Analyse carefully the context and provide a direct answer based on the context. If the user said Bonjour or Hello your only answer will be Hi! comment puis-je vous aider?
Answer in french only
{context}
Vous devez répondre aux questions en français.
### QUESTION:
{question}
[/INST]
Answer in french only
Vous devez répondre aux questions en français.
"""
repo_id = "mistralai/Mistral-7B-Instruct-v0.3"
# Load the model only once
if 'mistral_llm' not in st.session_state:
st.session_state.mistral_llm = HuggingFaceEndpoint(
repo_id=repo_id,
max_length=2048,
temperature=0.05,
huggingfacehub_api_token=st.secrets["HF_TOKEN"]
)
# Create prompt and LLM chain
prompt = PromptTemplate(
input_variables=["question"],
template=prompt_template,
)
llm_chain = LLMChain(llm=st.session_state.mistral_llm, prompt=prompt)
# Create QA chain
qa = RetrievalQA.from_chain_type(
llm=st.session_state.mistral_llm,
chain_type="stuff",
retriever=retriever,
chain_type_kwargs={"prompt": prompt},
)
# Streamlit UI setup
st.set_page_config(page_title="Alter-IA Chat", page_icon="🤖")
# Define function to handle user input and display chatbot response
def chatbot_response(user_input):
response = qa.run(user_input)
return response
# Define function to save feedback to CSV
def save_feedback(question, response, rating, comment):
try:
filename = '/tmp/feedback.csv' # Use /tmp directory for temporary storage in Spaces
file_exists = os.path.isfile(filename)
with open(filename, 'a', newline='', encoding='utf-8') as csvfile:
fieldnames = ['question', 'response', 'rating', 'comment']
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
if not file_exists:
writer.writeheader()
writer.writerow({'question': question, 'response': response, 'rating': rating, 'comment': comment})
st.success("Thank you for your feedback! It has been saved.")
except Exception as e:
st.error(f"Error saving feedback: {e}")
# Use session state to store user input, bot response, rating, and comment
if 'user_input' not in st.session_state:
st.session_state.user_input = ""
if 'bot_response' not in st.session_state:
st.session_state.bot_response = ""
if 'rating' not in st.session_state:
st.session_state.rating = 3 # Default rating
if 'comment' not in st.session_state:
st.session_state.comment = ""
# Create columns for logos
col1, col2, col3 = st.columns([2, 3, 2])
with col1:
st.image("Design 3_22.png", width=150, use_column_width=True)
with col3:
st.image("Altereo logo 2023 original - eau et territoires durables.png", width=150, use_column_width=True)
# Add CSS for styling
st.markdown("""
<style>
.centered-text {
text-align: center;
}
.centered-orange-text {
text-align: center;
color: darkorange;
}
</style>
""", unsafe_allow_html=True)
# Center and color text
st.markdown('<h3 class="centered-text">🤖 AlteriaChat 🤖 </h3>', unsafe_allow_html=True)
st.markdown('<p class="centered-orange-text">"Votre Réponse à Chaque Défi Méthodologique "</p>', unsafe_allow_html=True)
# Input and button for user interaction
st.session_state.user_input = st.text_input("You:", st.session_state.user_input)
if st.button("Ask 📨"):
if st.session_state.user_input.strip() != "":
st.session_state.bot_response = chatbot_response(st.session_state.user_input)
if st.session_state.bot_response:
st.markdown("### Bot:")
st.text_area("", value=st.session_state.bot_response, height=600)
# Feedback Section
st.markdown("### Évaluation de la réponse")
st.session_state.rating = st.slider("Rating (1 to 5)", 1, 5, st.session_state.rating)
st.session_state.comment = st.text_area("Your comment:", st.session_state.comment)
if st.button("Submit Feedback"):
if st.session_state.comment.strip() != "":
save_feedback(st.session_state.user_input, st.session_state.bot_response, st.session_state.rating, st.session_state.comment)
else:
st.warning("⚠️ Please enter a comment.")
# Motivational quote at the bottom
st.markdown("---")
st.markdown("La collaboration est la clé du succès. Chaque question trouve sa réponse, chaque défi devient une opportunité.")
|