Update app.py

app.py

@@ -1,16 +1,5 @@
-import streamlit as st
-from transformers import pipeline
-from scraper import scrape_website
-from googletrans import Translator
-from gtts import gTTS
-import speech_recognition as sr
-from langdetect import detect
-from io import BytesIO
-from pydub import AudioSegment
-import os
-
-##########
 import asyncio
+import streamlit as st
 import httpx
 
 # Define an async function to fetch data
@@ -19,80 +8,22 @@ async def fetch_data(url):
         response = await client.get(url)
         return response.text  # Return the response text
 
-# Create another async function for the chatbot to run
-async def main():
-    url = "https://www.sbbusba.edu.pk/"
-    response_text = await fetch_data(url)  # Use await inside an async function
-    print(response_text)  # Or process the response as needed
-
-# Run the async function using asyncio.run()
-if __name__ == "__main__":
-    asyncio.run(main())  # This is where the async function is actually executed
-
-###########
-
-# Initialize components
-translator = Translator()
-qa_pipeline = pipeline("question-answering", model="distilbert-base-uncased")
+# Define a wrapper function for asyncio to run inside Streamlit
+def get_data(url):
+    return asyncio.run(fetch_data(url))  # Run async function inside sync context
 
-# Function to process text-based interaction
-def process_text(user_input, context, lang):
-    try:
-        # Translate to English for the model
-        translated_input = translator.translate(user_input, src=lang, dest='en').text
-        answer = qa_pipeline({'question': translated_input, 'context': context})['answer']
-        # Translate answer back to user's language
-        translated_answer = translator.translate(answer, src='en', dest=lang).text
-        return translated_answer
-    except Exception as e:
-        return f"Error: {e}"
+# Streamlit app
+def main():
+    st.title("Custom Multilingual Chatbot")
 
-# Function to process voice input
-def process_voice(file, context):
-    recognizer = sr.Recognizer()
-    audio = sr.AudioFile(file)
-    with audio as source:
-        audio_data = recognizer.record(source)
-    user_input = recognizer.recognize_google(audio_data)
-    lang = detect(user_input)
-    return process_text(user_input, context, lang)
+    # User input for chatbot
+    user_input = st.text_input("Ask me anything:")
 
-# Streamlit UI
-st.title("Multilingual Chatbot with Web Scraping")
-st.sidebar.title("Options")
-
-# Scraping options
-url = st.sidebar.text_input("https://www.sbbusba.edu.pk/")
-if st.sidebar.button("Scrape Website"):
-    context = scrape_website(url)
-    st.sidebar.success("Website content scraped successfully!")
-else:
-    context = "No content available. Please scrape a website first."
-
-# Interaction options
-st.subheader("Chat with the Bot")
-mode = st.radio("Choose interaction mode:", ["Text", "Voice"])
-
-if mode == "Text":
-    user_input = st.text_input("Enter your query:")
-    if st.button("Ask"):
-        lang = detect(user_input)
-        response = process_text(user_input, context, lang)
-        st.write(f"Bot: {response}")
-
-elif mode == "Voice":
-    uploaded_file = st.file_uploader("Upload a voice file (WAV format):", type="wav")
-    if uploaded_file and st.button("Ask"):
-        response = process_voice(uploaded_file, context)
-        st.write(f"Bot: {response}")
-
-# Voice Output
-if st.button("Play Response as Voice"):
-    if response:
-        tts = gTTS(text=response, lang=lang)
-        audio_file = BytesIO()
-        tts.write_to_fp(audio_file)
-        st.audio(audio_file.getvalue(), format="audio/mp3")
-    else:
-        st.warning("No response to play!")
+    if user_input:
+        # Example: Use the get_data function to fetch data from a website
+        url = "https://www.sbbusba.edu.pk/"
+        response_text = get_data(url)  # Get the data using the sync wrapper
+        st.write(response_text)  # Show the fetched data in the Streamlit app
 
+if __name__ == "__main__":
+    main()