add llm to front + api key in dot env

.env

@@ -0,0 +1 @@
+MISTRAL_API_KEY = "i5jSJkCFNGKfgIztloxTMjfckiFbYBj4"

Modules/rag.py

@@ -1,11 +1,10 @@
 import os
 os.environ['TOKENIZERS_PARALLELISM'] = 'true'
-os.environ['MISTRAL_API_KEY'] = "i5jSJkCFNGKfgIztloxTMjfckiFbYBj4"
-# os.environ['OPENAI_API_KEY'] = "sk-proj-2WJfO8JpVyrdIeJ8QsO0T3BlbkFJWLhZF1xMlRZVFjNBccWh"
-os.environ['TAVILY_API_KEY'] = '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'
+from dotenv import load_dotenv
+load_dotenv() # load .env api keys 
 
 mistral_api_key = os.getenv("MISTRAL_API_KEY")
-tavily_api_key = os.getenv("TAVILY_API_KEY")
+
 from langchain_community.document_loaders import PyPDFLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.document_loaders import WebBaseLoader
@@ -19,7 +18,6 @@ from langchain_mistralai import ChatMistralAI
 from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
 from langchain_community.tools import DuckDuckGoSearchRun
 
-
 def load_chunk_persist_pdf() -> Chroma:
     pdf_folder_path = "data/pdf_folder/"
     documents = []
@@ -30,7 +28,7 @@ def load_chunk_persist_pdf() -> Chroma:
             documents.extend(loader.load())
     text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=10)
     chunked_documents = text_splitter.split_documents(documents)
-
+    os.makedirs("data/chroma_store/", exist_ok=True)
     vectorstore = Chroma.from_documents(
         documents=chunked_documents,
         embedding=MistralAIEmbeddings(),

app.py

@@ -2,11 +2,17 @@ import streamlit as st
 from st_audiorec import st_audiorec
 from Modules.Speech2Text.transcribe import transcribe
 import base64
+from langchain_mistralai import ChatMistralAI
+from dotenv import load_dotenv
+load_dotenv() # load .env api keys 
+import os
+mistral_api_key = os.getenv("MISTRAL_API_KEY")
 
 st.set_page_config(layout="wide", initial_sidebar_state="collapsed")
 # Create two columns
 col1, col2 = st.columns(2)
 video_uploaded = None
+llm = ChatMistralAI(model="mistral-large-latest", mistral_api_key=mistral_api_key, temperature=0)
 
 # First column containers
 with col1:
@@ -37,8 +43,10 @@ with col1:
 
         with st.chat_message("assistant"):
             # Build answer from LLM
-            response = " to be DEFINED "# TO DO 
-        st.session_state.messages.append({"role": "assistant", "content": response})
+            
+            response = llm.invoke(st.session_state.messages).content
+            st.session_state.messages.append({"role": "assistant", "content": response})
+            st.markdown(response)
 
     st.subheader("Movement Analysis")
         # TO DO