Update app.py

app.py

@@ -1,7 +1,7 @@
 import os
 import logging
 import re
-from langchain.vectorstores import Chroma
+from langchain_community.vectorstores import Chroma  # Updated import
 from langchain_huggingface import HuggingFaceEmbeddings
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_groq import ChatGroq
@@ -105,23 +105,19 @@ def create_rag_pipeline(file_paths, model, temperature, max_tokens):
         embedding=embedding_model,
         persist_directory="/tmp/chroma_db"
     )
-    vectorstore.persist()
+    # vectorstore.persist()  # Deprecated in Chroma 0.4.x
 
     retriever = vectorstore.as_retriever()
 
-    # Updated Prompt Template with Formatting Instructions
     custom_prompt_template = PromptTemplate(
         input_variables=["context", "question"],
         template="""
-You are an AI assistant specialized in daily wellness. Provide a concise, thorough, and stand-alone answer to the user's question based on the given context. Use Markdown formatting to enhance readability. For list-based answers, use numbered lists. Include relevant examples or schedules where beneficial. The final answer should be coherent, self-contained, and end with a complete sentence.
-
-**Context:**
+You are an AI assistant specialized in daily wellness. Provide a concise, thorough, and stand-alone answer to the user's question based on the given context. Include relevant examples or schedules where beneficial. **When listing steps or guidelines, format them as a numbered list with appropriate markdown formatting.** The final answer should be coherent, self-contained, and end with a complete sentence.
+Context:
 {context}
-
-**Question:**
+Question:
 {question}
-
-**Final Answer:**
+Final Answer:
 """
     )
 
@@ -146,7 +142,9 @@ def answer_question(model, temperature, max_tokens, question):
         return "The system is currently unavailable. Please try again later."
     try:
         answer = rag_chain.run(question)
-        complete_answer = ensure_complete_sentences(answer)
+        # Remove or modify ensure_complete_sentences if necessary
+        # complete_answer = ensure_complete_sentences(answer)
+        complete_answer = answer
         return complete_answer
     except Exception as e_inner:
         logger.error(f"Error: {e_inner}")
@@ -155,7 +153,6 @@ def answer_question(model, temperature, max_tokens, question):
 def gradio_interface(model, temperature, max_tokens, question):
     return answer_question(model, temperature, max_tokens, question)
 
-# Updated Gradio Interface to Render Markdown
 interface = gr.Interface(
     fn=gradio_interface,
     inputs=[
@@ -164,11 +161,11 @@ interface = gr.Interface(
         gr.Slider(label="Max Tokens", minimum=200, maximum=2048, step=1, value=max_tokens),
         gr.Textbox(label="Question", placeholder="e.g., What is box breathing and how does it help reduce anxiety?")
     ],
-    outputs=gr.outputs.Markdown(),  # Changed from "text" to Markdown
+    outputs=gr.Markdown(label="Answer"),  # Updated output component
     title="Daily Wellness AI",
-    description="Ask questions about daily wellness and receive a concise, complete, and well-formatted answer.",
+    description="Ask questions about daily wellness and receive a concise, complete answer.",
     examples=[
-        ["llama3-8b-8192", 0.7, 500, "What are 10 steps for yoga?"],
+        ["llama3-8b-8192", 0.7, 500, "What is box breathing and how does it help reduce anxiety?"],
         ["llama3-8b-8192", 0.6, 600, "Give me a weekly fitness schedule incorporating mindfulness exercises."]
     ],
     allow_flagging="never"