Update app.py

app.py

@@ -1,44 +1,56 @@
 import os
 import streamlit as st
-import pandas as pd
 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
-from huggingface_hub import login
-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.chains import LLMChain, RetrievalQA
+import gspread
+from oauth2client.service_account import ServiceAccountCredentials
+import json
+
+import gspread
+from oauth2client.service_account import ServiceAccountCredentials
+import json
+
+# Load Google service account credentials from Hugging Face secrets
+GOOGLE_SERVICE_ACCOUNT_JSON = st.secrets["GOOGLE_SERVICE_ACCOUNT_JSON"]
+
+# Google Sheets setup
+scope = ["https://www.googleapis.com/auth/spreadsheets", "https://www.googleapis.com/auth/drive"]
+service_account_info = json.loads(GOOGLE_SERVICE_ACCOUNT_JSON)
+creds = ServiceAccountCredentials.from_json_keyfile_dict(service_account_info, scope)
+client = gspread.authorize(creds)
+sheet = client.open("users feedback").sheet1  # Replace with your Google Sheet name
+# Function to save user feedback to Google Sheets
+def save_feedback(user_input, bot_response, rating, comment):
+    feedback = [user_input, bot_response, rating, comment]
+    sheet.append_row(feedback)
 
-# Authenticate with Hugging Face
+# Hugging Face API login
+from huggingface_hub import login
 login(token=st.secrets["HF_TOKEN"])
 
-# Load FAISS index
+# Initialize LangChain components
 db = FAISS.load_local("faiss_index", HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L12-v2'), allow_dangerous_deserialization=True)
-
-# Set up retriever
 retriever = db.as_retriever(search_type="mmr", search_kwargs={'k': 1})
 
-# Prompt template for the LLM
 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.
-
+        Analyse carefully the context and provide a direct answer based on the context. If the user says 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.
+Answer in french only
+ Vous devez répondre aux questions en français.
 """
 
-# Set up the LLM from Hugging Face
 repo_id = "mistralai/Mistral-7B-Instruct-v0.3"
 
 mistral_llm = HuggingFaceEndpoint(
@@ -51,11 +63,10 @@ prompt = PromptTemplate(
     template=prompt_template,
 )
 
-# Create LLM chain
+# Create llm chain
 llm_chain = LLMChain(llm=mistral_llm, prompt=prompt)
 
-# Set up RetrievalQA chain
-retriever.search_kwargs = {'k': 1}
+# Create RetrievalQA chain
 qa = RetrievalQA.from_chain_type(
     llm=mistral_llm,
     chain_type="stuff",
@@ -63,52 +74,116 @@ qa = RetrievalQA.from_chain_type(
     chain_type_kwargs={"prompt": prompt},
 )
 
-# Streamlit interface setup
+# Streamlit interface with improved aesthetics
 st.set_page_config(page_title="Alter-IA Chat", page_icon="🤖")
 
-# Function to handle user input and display chatbot response
+# Define function to handle user input and display chatbot response
 def chatbot_response(user_input):
     response = qa.run(user_input)
     return response
 
-import gspread
-from oauth2client.service_account import ServiceAccountCredentials
-import json
-
-# Load Google service account credentials from Hugging Face secrets
-GOOGLE_SERVICE_ACCOUNT_JSON = st.secrets["GOOGLE_SERVICE_ACCOUNT_JSON"]
-
-# Google Sheets setup
-scope = ["https://www.googleapis.com/auth/spreadsheets", "https://www.googleapis.com/auth/drive"]
-service_account_info = json.loads(GOOGLE_SERVICE_ACCOUNT_JSON)
-creds = ServiceAccountCredentials.from_json_keyfile_dict(service_account_info, scope)
-client = gspread.authorize(creds)
-sheet = client.open("users feedback").sheet1  # Replace with your Google Sheet name
-
-# Function to save user feedback to Google Sheets
-def save_feedback(user_input, bot_response, rating, comment):
-    feedback = [user_input, bot_response, rating, comment]
-    sheet.append_row(feedback)
-
-# Streamlit app layout
-
-st.markdown("<h3 style='text-align: center;'>🤖 Chatbot Feedback 🤖</h3>", unsafe_allow_html=True)
-
-user_input = st.text_input("You:")
-bot_response = "This is a bot response."  # Replace this with your chatbot's response logic
-
-st.markdown("### Rate the response:")
-rating = st.selectbox("", [1, 2, 3, 4, 5])
-
-st.markdown("### Leave a comment:")
-comment = st.text_area("")
-
-if st.button("Submit"):
-    if user_input.strip() and comment.strip():
-        save_feedback(user_input, bot_response, rating, comment)
-        st.success("Thank you for your feedback!")
-    else:
-        st.warning("Please provide both input and 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
+
+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
+st.markdown("""
+    <style>
+    .centered-text {
+        text-align: center;
+    }
+    .stars {
+        font-size: 24px;
+        color: lightgray;
+        cursor: pointer;
+    }
+    .stars:hover,
+    .stars.selected {
+        color: gold;
+    }
+    .stars.filled {
+        color: gold;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+# Star rating system with JavaScript
+st.markdown("""
+    <script>
+    function setRating(starId) {
+        const stars = document.querySelectorAll('.stars');
+        stars.forEach(star => star.classList.remove('filled'));
+        document.querySelectorAll('.stars').forEach(star => {
+            if (star.dataset.rating <= starId) {
+                star.classList.add('filled');
+            }
+        });
+        document.getElementById('rating').value = starId;
+    }
+    document.addEventListener('DOMContentLoaded', (event) => {
+        document.querySelectorAll('.stars').forEach(star => {
+            star.addEventListener('click', () => {
+                setRating(star.dataset.rating);
+            });
+        });
+    });
+    </script>
+    """, unsafe_allow_html=True)
+
+# Display star rating
+st.markdown('<div class="stars" data-rating="1">&#9733;</div>', unsafe_allow_html=True)
+st.markdown('<div class="stars" data-rating="2">&#9733;</div>', unsafe_allow_html=True)
+st.markdown('<div class="stars" data-rating="3">&#9733;</div>', unsafe_allow_html=True)
+st.markdown('<div class="stars" data-rating="4">&#9733;</div>', unsafe_allow_html=True)
+st.markdown('<div class="stars" data-rating="5">&#9733;</div>', unsafe_allow_html=True)
+
+# Hidden input field to store the rating value
+st.markdown('<input type="hidden" id="rating" value="0">', 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 form
+        st.markdown("### Rate the response:")
+        st.markdown('<p>Please click on the stars to rate the response.</p>', unsafe_allow_html=True)
+        st.markdown('<p id="rating-value">Rating: 0</p>', unsafe_allow_html=True)
+
+        st.markdown("### Leave a comment:")
+        comment = st.text_area("")
+
+        # Update rating value on star click
+        st.markdown("""
+            <script>
+            document.querySelectorAll('.stars').forEach(star => {
+                star.addEventListener('click', function() {
+                    document.getElementById('rating-value').innerText = 'Rating: ' + this.dataset.rating;
+                });
+            });
+            </script>
+            """, unsafe_allow_html=True)
+
+        # Feedback submission
+        if st.button("Submit Feedback"):
+            rating = st.text_input("Rating", value="0")
+            if comment.strip() and rating != "0":
+                save_feedback(user_input, bot_response, rating, comment)
+                st.success("Thank you for your feedback!")
+            else:
+                st.warning("⚠️ Please provide a comment and a rating.")
+
+# Motivational quote at the bottom
 st.markdown("---")
-st.markdown("Collaboration is the key to success. Each question finds its answer, each challenge becomes an opportunity.")
+st.markdown("La collaboration est la clé du succès. Chaque question trouve sa réponse, chaque défi devient une opportunité.")