Update app.py

app.py

@@ -1,68 +1,59 @@
 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.schema.runnable import RunnablePassthrough
-from langchain.chains import LLMChain
-
+from langchain.chains import LLMChain, RetrievalQA
 from huggingface_hub import login
-login(token=st.secrets["HF_TOKEN"])
-
-from langchain_community.document_loaders import TextLoader
-from langchain_text_splitters import CharacterTextSplitter
-from langchain_community.document_loaders import PyPDFLoader
-from langchain.chains import RetrievalQA
-from langchain.prompts import PromptTemplate
-from langchain.embeddings.huggingface import HuggingFaceEmbeddings
-
-db = FAISS.load_local("faiss_index", HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L12-v2'),allow_dangerous_deserialization=True)
 
+# Login to Hugging Face
+login(token=st.secrets["HF_TOKEN"])
 
+# Load FAISS index
+db = FAISS.load_local(
+    "faiss_index", HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L12-v2'),
+    allow_dangerous_deserialization=True
+)
 
+# Create retriever
 retriever = 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?
+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"
 
+# Create LLM model
 mistral_llm = HuggingFaceEndpoint(
     repo_id=repo_id, max_length=2048, temperature=0.05, huggingfacehub_api_token=st.secrets["HF_TOKEN"]
 )
 
-# Create prompt from prompt template
+# Create prompt and LLM chain
 prompt = PromptTemplate(
     input_variables=["question"],
     template=prompt_template,
 )
-
-# Create llm chain
 llm_chain = LLMChain(llm=mistral_llm, prompt=prompt)
 
-
-retriever.search_kwargs = {'k':1}
+# Create QA chain
 qa = RetrievalQA.from_chain_type(
     llm=mistral_llm,
     chain_type="stuff",
@@ -70,9 +61,7 @@ qa = RetrievalQA.from_chain_type(
     chain_type_kwargs={"prompt": prompt},
 )
 
-import streamlit as st
-
-# Streamlit interface with improved aesthetics
+# Streamlit UI setup
 st.set_page_config(page_title="Alter-IA Chat", page_icon="🤖")
 
 # Define function to handle user input and display chatbot response
@@ -80,30 +69,32 @@ 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):
+    filename = 'feedback.csv'
+    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})
 
 # 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)  # Adjust image path and size as needed
+    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)  # Adjust image path and size as needed
-# Streamlit components
-# Ajouter un peu de CSS pour centrer le texte
-# Ajouter un peu de CSS pour centrer le texte et le colorer en orange foncé
+    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;
     }
-    </style>
-    """, unsafe_allow_html=True)
-
-# Utiliser la classe CSS pour centrer et colorer le texte
-st.markdown('<h3 class="centered-text">🤖 AlteriaChat 🤖 </h3>', unsafe_allow_html=True)
-st.markdown("""
-    <style>
     .centered-orange-text {
         text-align: center;
         color: darkorange;
@@ -111,23 +102,35 @@ st.markdown("""
     </style>
     """, unsafe_allow_html=True)
 
-# Centrer le texte principal
-# Centrer et colorer en orange foncé le texte spécifique
+# 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
 user_input = st.text_input("You:", "")
 submit_button = st.button("Ask 📨")
 
-# Handle user input
 if submit_button:
     if user_input.strip() != "":
         bot_response = chatbot_response(user_input)
         st.markdown("### Bot:")
         st.text_area("", value=bot_response, height=600)
+
+        # Feedback Section
+        st.markdown("### Évaluation de la réponse")
+        rating = st.slider("Rating (1 to 5)", 1, 5, 3)
+        comment = st.text_area("Your comment:", "")
+        
+        if st.button("Submit Feedback"):
+            if comment.strip() != "":
+                save_feedback(user_input, bot_response, rating, comment)
+                st.success("Thank you for your feedback!")
+            else:
+                st.warning("⚠️ Please enter a comment.")
+
     else:
         st.warning("⚠️ Please enter a message.")
 
 # 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.")
-