Update app.py

app.py

@@ -6,6 +6,7 @@ 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
 
 # Set up working directory and API configuration
 working_dir = os.path.dirname(os.path.abspath(__file__))
@@ -46,21 +47,86 @@ def chat_chain(vectorstore):
     )
     return chain
 
+def transcribe_audio(selected_language):
+    """Function to capture and transcribe audio in the 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)  # 5 seconds to start speaking
+                query = recognizer.recognize_google(audio, language=selected_language)  # Transcribe audio in 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
+
 # Streamlit UI
-st.title("Bhagavad Gita & Yoga Sutras Query Assistant")
+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)
 
 vectorstore = setup_vectorstore()
 chain = chat_chain(vectorstore)
 
-# User input
-user_query = st.text_input("Ask a question about the Bhagavad Gita or Yoga Sutras:")
+# User input options
+st.write("You can either type your question or use voice search:")
+st.markdown("### 📝 Type your query or 🎙️ Use voice search")
+
+# Multilingual support: Select language for voice input
+language_options = {
+    "English": "en-US",
+    "Hindi": "hi-IN",
+    "Spanish": "es-ES",
+    "French": "fr-FR",
+    "German": "de-DE"
+}
+selected_language = st.selectbox("Select your language for voice search:", options=list(language_options.keys()))
+language_code = language_options[selected_language]
+
+if st.button("🎙️ Use Voice Search"):
+    user_query = transcribe_audio(language_code)
+else:
+    user_query = st.text_input("Ask a question about the Bhagavad Gita or Yoga Sutras:")
+
 if user_query:
     # Use `__call__` to get all outputs as a dictionary
     response = chain({"question": user_query})
     answer = response.get("answer", "No answer found.")
     source_documents = response.get("source_documents", [])
 
-    st.write(f"**Answer:** {answer}")
-    st.write(f"**Source Documents:**")
+    st.markdown("### ✅ **Answer:**")
+    st.write(answer)
+    st.markdown("### 📄 **Source Documents:**")
     for doc in source_documents:
         st.write(doc)