Update app.py

app.py

@@ -1,164 +1,164 @@
-import streamlit as st
-from dotenv import load_dotenv
-from audiorecorder import audiorecorder
-from langchain_core.messages import HumanMessage, AIMessage
-import requests
-from transformers import pipeline
-from gtts import gTTS
-import io
-from langchain_core.runnables.base import RunnableSequence
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_groq import ChatGroq
-import os
-import requests
-from dotenv import load_dotenv
-from langgraph.checkpoint.memory import MemorySaver
-from langgraph.prebuilt import create_react_agent
-from langchain_community.tools.tavily_search import TavilySearchResults
-
-st.set_page_config(page_title="Urdu Virtual Assistant", page_icon="🤖")  # set the page title and icon
-
-# Load environment variables (if any)
-load_dotenv()
-
-user_id = "1"  # example user id
-
-llm = ChatGroq(
-    model="llama-3.1-70b-versatile",
-    temperature=0,
-    max_tokens=None,
-    timeout=None,
-    max_retries=5,
-    groq_api_key=os.getenv("GROQ_API_KEY")
-)
-
-search = TavilySearchResults(
-      max_results=2,
-    )
-tools = [search]
-
-agent_executor = create_react_agent(llm, tools)
-
-# Initialize the wav2vec2 model for Urdu speech-to-text
-pipe = pipeline("automatic-speech-recognition", model="kingabzpro/wav2vec2-large-xls-r-300m-Urdu")
-
-def translate(target, text):
-  '''
-    Translates given text into target language
-
-    Parameters:
-        target (string): 2 character code to specify the target language.
-        text (string): Text to be translated.
-
-    Returns:
-        res (string): Translated text.
-  '''
-  url = "https://microsoft-translator-text.p.rapidapi.com/translate"
-
-  querystring = {"api-version":"3.0","profanityAction":"NoAction","textType":"plain", "to":target}
-
-  payload = [{ "Text": text }]
-  headers = {
-    "x-rapidapi-key": os.getenv("RAPIDAPI_LANG_TRANS"),
-    "x-rapidapi-host": "microsoft-translator-text.p.rapidapi.com",
-    "Content-Type": "application/json"
-  }
-
-  response = requests.post(url, json=payload, headers=headers, params=querystring)
-  res = response.json()
-  return res[0]["translations"][0]["text"]
-
-
-def infer(user_input: str):
-    '''
-        Returns the translated response from the LLM in response to a user query.
-
-        Parameters:
-            user_id (string): User ID of a user.
-            user_input (string): User query.
-
-        Returns:
-            res (string): Returns a translated response from the LLM.
-    '''
-
-    user_input = translate("en", user_input) # translate user query to english
-
-    prompt = ChatPromptTemplate.from_messages( # define a prompt
-        [
-            (
-                "system",
-                "You are a compassionate and friendly AI virtual assistant. You will provide helpful answers to user queries using the provided tool to ensure the accuracy and relevance of your responses."
-            ),
-            ("human", "{user_input}")
-        ]
-    )
-
-    runnable = prompt | agent_executor # define a chain
-
-    conversation = RunnableSequence( # wrap the chain along with chat history and user input
-        runnable,
-    )
-
-    response = conversation.invoke( # invoke the chain by giving the user input and the chat history
-        {"user_input": user_input},
-    )
-
-    res = translate("ur", response["messages"][-1].content) # translate the response to Urdu
-    return res
-
-
-def text_to_speech(text, lang='ur'):
-    '''
-        Converts text to speech using gTTS.
-
-        Parameters:
-            text (string): Text to be converted to speech.
-            lang (string): Language for the speech synthesis. Default is 'ur' (Urdu).
-        Returns:
-            response_audio_io (BytesIO): BytesIO object containing the audio data.
-    '''
-    tts = gTTS(text, lang=lang)
-    response_audio_io = io.BytesIO()
-    tts.write_to_fp(response_audio_io)
-    response_audio_io.seek(0)
-    return response_audio_io
-
-
-col1, col2 = st.columns([1, 5])  # Adjust the ratio to control the logo and title sizes
-
-# Display the logo in the first column
-with col1:
-    st.image("bolo_logo-removebg-preview.png", width=100)  # Adjust the width as needed
-
-# Display the title in the second column
-with col2:
-    st.title("Urdu Virtual Assistant") # set the main title of the application
-st.write("This application is a comprehensive speech-to-speech model designed to understand and respond in Urdu. It not only handles natural conversations but also has the capability to access and provide real-time information by integrating with the Tavily search engine. Whether you're asking for the weather or engaging in everyday dialogue, this assistant delivers accurate and context-aware responses, all in Urdu.")
-
-# Add a text input box
-audio = audiorecorder()
-
-if len(audio) > 0:
-    # Save the audio to a file
-    audio.export("audio.wav", format="wav")
-
-    # Convert audio to text using the wav2vec2 model
-    with open("audio.wav", "rb") as f:
-        audio_bytes = f.read()
-
-    # Process the audio file
-    result = pipe("audio.wav")
-    user_query = result["text"]
-
-    with st.chat_message("Human"):  # create the message box for human input
-        st.audio(audio.export().read())  # display the audio player
-        st.markdown(user_query)
-
-    # Get response from the LLM
-    response_text = infer(user_input=user_query)
-    response_audio = text_to_speech(response_text, lang='ur')
-
-    # Play the generated speech in the app
-    with st.chat_message("AI"):
-      st.audio(response_audio.read(), format='audio/mp3')
+import streamlit as st
+from dotenv import load_dotenv
+from audiorecorder import audiorecorder
+from langchain_core.messages import HumanMessage, AIMessage
+import requests
+from transformers import pipeline
+from gtts import gTTS
+import io
+from langchain_core.runnables.base import RunnableSequence
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_groq import ChatGroq
+import os
+import requests
+from dotenv import load_dotenv
+from langgraph.checkpoint.memory import MemorySaver
+from langgraph.prebuilt import create_react_agent
+from langchain_community.tools.tavily_search import TavilySearchResults
+
+st.set_page_config(page_title="Urdu Virtual Assistant", page_icon="🤖")  # set the page title and icon
+
+# Load environment variables (if any)
+load_dotenv()
+
+user_id = "1"  # example user id
+
+llm = ChatGroq(
+    model="llama-3.1-70b-versatile",
+    temperature=0.3,
+    max_tokens=None,
+    timeout=None,
+    max_retries=5,
+    groq_api_key=os.getenv("GROQ_API_KEY")
+)
+
+search = TavilySearchResults(
+      max_results=2,
+    )
+tools = [search]
+
+agent_executor = create_react_agent(llm, tools)
+
+# Initialize the wav2vec2 model for Urdu speech-to-text
+pipe = pipeline("automatic-speech-recognition", model="kingabzpro/wav2vec2-large-xls-r-300m-Urdu")
+
+def translate(target, text):
+  '''
+    Translates given text into target language
+
+    Parameters:
+        target (string): 2 character code to specify the target language.
+        text (string): Text to be translated.
+
+    Returns:
+        res (string): Translated text.
+  '''
+  url = "https://microsoft-translator-text.p.rapidapi.com/translate"
+
+  querystring = {"api-version":"3.0","profanityAction":"NoAction","textType":"plain", "to":target}
+
+  payload = [{ "Text": text }]
+  headers = {
+    "x-rapidapi-key": os.getenv("RAPIDAPI_LANG_TRANS"),
+    "x-rapidapi-host": "microsoft-translator-text.p.rapidapi.com",
+    "Content-Type": "application/json"
+  }
+
+  response = requests.post(url, json=payload, headers=headers, params=querystring)
+  res = response.json()
+  return res[0]["translations"][0]["text"]
+
+
+def infer(user_input: str):
+    '''
+        Returns the translated response from the LLM in response to a user query.
+
+        Parameters:
+            user_id (string): User ID of a user.
+            user_input (string): User query.
+
+        Returns:
+            res (string): Returns a translated response from the LLM.
+    '''
+
+    user_input = translate("en", user_input) # translate user query to english
+
+    prompt = ChatPromptTemplate.from_messages( # define a prompt
+        [
+            (
+                "system",
+                "You are a compassionate and friendly AI virtual assistant. You will provide helpful answers to user queries using the provided tool to ensure the accuracy and relevance of your responses."
+            ),
+            ("human", "{user_input}")
+        ]
+    )
+
+    runnable = prompt | agent_executor # define a chain
+
+    conversation = RunnableSequence( # wrap the chain along with chat history and user input
+        runnable,
+    )
+
+    response = conversation.invoke( # invoke the chain by giving the user input and the chat history
+        {"user_input": user_input},
+    )
+
+    res = translate("ur", response["messages"][-1].content) # translate the response to Urdu
+    return res
+
+
+def text_to_speech(text, lang='ur'):
+    '''
+        Converts text to speech using gTTS.
+
+        Parameters:
+            text (string): Text to be converted to speech.
+            lang (string): Language for the speech synthesis. Default is 'ur' (Urdu).
+        Returns:
+            response_audio_io (BytesIO): BytesIO object containing the audio data.
+    '''
+    tts = gTTS(text, lang=lang)
+    response_audio_io = io.BytesIO()
+    tts.write_to_fp(response_audio_io)
+    response_audio_io.seek(0)
+    return response_audio_io
+
+
+col1, col2 = st.columns([1, 5])  # Adjust the ratio to control the logo and title sizes
+
+# Display the logo in the first column
+with col1:
+    st.image("bolo_logo-removebg-preview.png", width=100)  # Adjust the width as needed
+
+# Display the title in the second column
+with col2:
+    st.title("Urdu Virtual Assistant") # set the main title of the application
+st.write("This application is a comprehensive speech-to-speech model designed to understand and respond in Urdu. It not only handles natural conversations but also has the capability to access and provide real-time information by integrating with the Tavily search engine. Whether you're asking for the weather or engaging in everyday dialogue, this assistant delivers accurate and context-aware responses, all in Urdu.")
+
+# Add a text input box
+audio = audiorecorder()
+
+if len(audio) > 0:
+    # Save the audio to a file
+    audio.export("audio.wav", format="wav")
+
+    # Convert audio to text using the wav2vec2 model
+    with open("audio.wav", "rb") as f:
+        audio_bytes = f.read()
+
+    # Process the audio file
+    result = pipe("audio.wav")
+    user_query = result["text"]
+
+    with st.chat_message("Human"):  # create the message box for human input
+        st.audio(audio.export().read())  # display the audio player
+        st.markdown(user_query)
+
+    # Get response from the LLM
+    response_text = infer(user_input=user_query)
+    response_audio = text_to_speech(response_text, lang='ur')
+
+    # Play the generated speech in the app
+    with st.chat_message("AI"):
+      st.audio(response_audio.read(), format='audio/mp3')
       st.markdown(response_text)