Update app.py

app.py

@@ -11,14 +11,16 @@ from huggingface_hub import login
 # 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(
+# 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}
 )
@@ -26,8 +28,8 @@ retriever = db.as_retriever(
 # 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?
+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}
@@ -36,26 +38,30 @@ Vous devez répondre aux questions en français.
 {question}
 [/INST]
 Answer in french only
- Vous devez répondre aux questions en français.
+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"]
-)
+# 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=mistral_llm, prompt=prompt)
+llm_chain = LLMChain(llm=st.session_state.mistral_llm, prompt=prompt)
 
 # Create QA chain
 qa = RetrievalQA.from_chain_type(
-    llm=mistral_llm,
+    llm=st.session_state.mistral_llm,
     chain_type="stuff",
     retriever=retriever,
     chain_type_kwargs={"prompt": prompt},
@@ -71,14 +77,18 @@ def chatbot_response(user_input):
 
 # 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})
+    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}")
 
 # Create columns for logos
 col1, col2, col3 = st.columns([2, 3, 2])
@@ -124,7 +134,6 @@ if submit_button:
         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.")