Updated by Krish

app.py

@@ -1,3 +1,5 @@
+# NEW CODE
+
 import os
 import json
 import streamlit as st
@@ -5,6 +7,213 @@ from langchain_huggingface import HuggingFaceEmbeddings
 from langchain_chroma import Chroma
 from langchain.memory import ConversationBufferMemory
 from langchain.chains import ConversationalRetrievalChain
+from vectorize_documents import embeddings
+import speech_recognition as sr
+from deep_translator import GoogleTranslator
+
+# Set up working directory and API configuration
+working_dir = os.path.dirname(os.path.abspath(__file__))
+config_data = json.load(open(f"{working_dir}/config.json"))
+os.environ["GROQ_API_KEY"] = config_data["GROQ_API_KEY"]
+
+# Streamlit session state initialization
+def initialize_session_state():
+    if "chat_history" not in st.session_state:
+        st.session_state["chat_history"] = []
+    if "vectorstore" not in st.session_state:
+        st.session_state["vectorstore"] = setup_vectorstore()
+    if "chain" not in st.session_state:
+        st.session_state["chain"] = chat_chain(st.session_state["vectorstore"])
+
+# Vectorstore setup
+def setup_vectorstore():
+    embeddings = HuggingFaceEmbeddings()
+    vectorstore = Chroma(
+        persist_directory=f"{working_dir}/vector_db_dir",
+        embedding_function=embeddings
+    )
+    return vectorstore
+
+# Chat chain setup
+def chat_chain(vectorstore):
+    from langchain_groq import ChatGroq
+    llm = ChatGroq(
+        model="llama-3.1-70b-versatile",
+        temperature=0
+    )
+    retriever = vectorstore.as_retriever()
+    memory = ConversationBufferMemory(
+        memory_key="chat_history",
+        return_messages=True
+    )
+    chain = ConversationalRetrievalChain.from_llm(
+        llm=llm,
+        retriever=retriever,
+        chain_type="stuff",
+        memory=memory,
+        verbose=True
+    )
+    return chain
+
+# Transcription function
+def transcribe_audio(selected_language):
+    try:
+        recognizer = sr.Recognizer()
+        with sr.Microphone() as source:
+            st.write("🎤 Listening... Please ask your question.")
+            try:
+                audio = recognizer.listen(source, timeout=5)
+                query = recognizer.recognize_google(audio, language=selected_language)
+                st.write(f"**🗣️ You said:** {query}")
+                return query
+            except sr.WaitTimeoutError:
+                st.error("⏳ You didn't speak in time. Please try again.")
+            except sr.UnknownValueError:
+                st.error("❌ Sorry, could not understand the audio. Please try again.")
+            except sr.RequestError as e:
+                st.error(f"⚠️ Error with speech recognition service: {e}")
+    except AttributeError:
+        st.error("❌ Microphone or PyAudio not available. Please check installation.")
+    except OSError as e:
+        st.error(f"⚠️ Audio input error: {e}")
+    return None
+
+# Translation functions
+def translate_to_english(text, source_lang):
+    if source_lang == "en":  # Skip translation if the language is English
+        return text
+    return GoogleTranslator(source=source_lang, target="en").translate(text)
+
+def translate_from_english(text, target_lang):
+    if target_lang == "en":  # Skip translation if the language is English
+        return text
+    return GoogleTranslator(source="en", target=target_lang).translate(text)
+
+# Streamlit UI
+initialize_session_state()
+
+st.markdown(
+    """
+    <style>
+    .main-title {
+        font-size: 36px;
+        color: #FF8C00;
+        font-weight: bold;
+    }
+    .sub-title {
+        font-size: 24px;
+        color: #FF8C00;
+    }
+    .icon {
+        font-size: 50px;
+        color: #FF8C00;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+st.markdown('<div class="icon">📚</div>', unsafe_allow_html=True)
+st.markdown('<div class="main-title">Bhagavad Gita & Yoga Sutras Query Assistant</div>', unsafe_allow_html=True)
+st.markdown('<div class="sub-title">Ask questions and explore timeless wisdom</div>', unsafe_allow_html=True)
+
+# Language support
+indian_languages = {
+    "English": "en",
+    "Assamese": "as",
+    "Bengali": "bn",
+    "Gujarati": "gu",
+    "Hindi": "hi",
+    "Kannada": "kn",
+    "Kashmiri": "ks",
+    "Konkani": "kok",
+    "Malayalam": "ml",
+    "Manipuri": "mni",
+    "Marathi": "mr",
+    "Nepali": "ne",
+    "Odia": "or",
+    "Punjabi": "pa",
+    "Sanskrit": "sa",
+    "Santali": "sat",
+    "Sindhi": "sd",
+    "Tamil": "ta",
+    "Telugu": "te",
+    "Urdu": "ur",
+    "Bodo": "brx",
+    "Dogri": "doi",
+    "Maithili": "mai",
+    "Santhali": "sat",
+    "Tulu": "tcy",
+    "Bhili/Bhilodi": "bhi",
+    "Khasi": "kha",
+    "Garo": "grt",
+    "Mizo": "lus",
+    "Sora": "srb",
+    "Ho": "hoc",
+    "Kurukh": "kru",
+    "Korwa": "kfa",
+    "Gondi": "gon",
+    "Konkani": "kok"
+}
+selected_language = st.selectbox("Select your language:", options=list(indian_languages.keys()))
+language_code = indian_languages[selected_language]
+
+# User-friendly input selection
+st.markdown("### How would you like to ask your question?")
+input_mode = st.radio("Choose input method:", ("Voice", "Typing"))
+
+user_query = None  # Initialize the variable to ensure it's always defined
+
+if input_mode == "Voice":
+    st.write("Click the button below to speak your question:")
+    if st.button("🎤 Use Voice Input"):
+        user_query = transcribe_audio(language_code)
+        if user_query:
+            user_query = translate_to_english(user_query, language_code)
+else:
+    user_query = st.text_input("Type your question here:")
+    if user_query:
+        user_query = translate_to_english(user_query, language_code)
+
+if user_query:  # This check will now always work
+    with st.spinner("Getting answer..."):
+        response = st.session_state["chain"]({"question": user_query})
+        relevant_content = response.get("source_documents", None)
+
+        if not relevant_content:
+            st.markdown("### ❌ **No Answer Available:**")
+            st.write("The system does not have sufficient information to answer this question.")
+        else:
+            answer = response.get("answer", None)
+            translated_answer = translate_from_english(answer, language_code)
+            st.markdown("### ✅ **Answer:**")
+            st.write(translated_answer)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+# OLD CODE IS BELOW 
+'''import os
+import json
+import streamlit as st
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_chroma import Chroma
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
 from vectorize_documents import embeddings  # Import embeddings from the vectorization script
 import speech_recognition as sr  # For voice recognition
 
@@ -130,3 +339,4 @@ if user_query:
     st.markdown("### 📄 **Source Documents:**")
     for doc in source_documents:
         st.write(doc)
+'''