Update app.py

app.py

@@ -1,91 +1,91 @@
-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
-
-# Load environment variables (if any)
-load_dotenv()
-
-user_id = "1"  # example user id
-
-# Initialize the wav2vec2 model for Urdu speech-to-text
-pipe = pipeline("automatic-speech-recognition", model="kingabzpro/wav2vec2-large-xls-r-300m-Urdu")
-
-def get_response(user_input):
-    '''
-        Takes user_input in English and invokes the infer API for response.
-
-        Parameters:
-            user_input (string): User Query in English.
-        Returns:
-            res (string): Response from the LLM.
-    '''
-    url = f"https://whole-icons-hammer.loca.lt/infer/{user_id}"
-    headers = {"Content-Type": "application/x-www-form-urlencoded"}
-    data = {"user_input": user_input}
-    response = requests.post(url, headers=headers, data=data)
-    res = response.json()
-    return res["data"]
-
-
-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
-
-
-st.set_page_config(page_title="Urdu Virtual Assistant", page_icon="🤖")  # set the page title and icon
-
-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 = get_response(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
+
+# Load environment variables (if any)
+load_dotenv()
+
+user_id = "1"  # example user id
+
+# Initialize the wav2vec2 model for Urdu speech-to-text
+pipe = pipeline("automatic-speech-recognition", model="kingabzpro/wav2vec2-large-xls-r-300m-Urdu")
+
+def get_response(user_input):
+    '''
+        Takes user_input in English and invokes the infer API for response.
+
+        Parameters:
+            user_input (string): User Query in English.
+        Returns:
+            res (string): Response from the LLM.
+    '''
+    url = f"http://127.0.0.1:8000/infer/{user_id}"
+    headers = {"Content-Type": "application/x-www-form-urlencoded"}
+    data = {"user_input": user_input}
+    response = requests.post(url, headers=headers, data=data)
+    res = response.json()
+    return res["data"]
+
+
+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
+
+
+st.set_page_config(page_title="Urdu Virtual Assistant", page_icon="🤖")  # set the page title and icon
+
+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 = get_response(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)