Update app.py

app.py

@@ -1,29 +1,89 @@
-import asyncio
 import streamlit as st
-import httpx
+import requests
+from bs4 import BeautifulSoup
+import speech_recognition as sr
+from gtts import gTTS
+import os
+from sentence_transformers import SentenceTransformer
+import faiss
+import numpy as np
+from transformers import pipeline
 
-# Define an async function to fetch data
-async def fetch_data(url):
-    async with httpx.AsyncClient() as client:
-        response = await client.get(url)
-        return response.text  # Return the response text
+# Scrape website data
+def scrape_website(url):
+    response = requests.get(url)
+    soup = BeautifulSoup(response.text, 'html.parser')
+    text = soup.get_text()
+    return text
 
-# 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 create embeddings
+def create_embeddings(texts):
+    model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
+    embeddings = model.encode(texts)
+    return embeddings
 
-# Streamlit app
+# Use Faiss for similarity search
+def search(query, documents, k=1):
+    embeddings = create_embeddings([query] + documents)
+    query_embedding = embeddings[0]
+    doc_embeddings = np.stack(embeddings[1:])
+    
+    index = faiss.IndexFlatL2(doc_embeddings.shape[1])  # L2 distance for similarity
+    index.add(doc_embeddings)
+    
+    # Search for the top-k most similar documents
+    D, I = index.search(np.array([query_embedding]), k)
+    return [documents[i] for i in I[0]]
+
+# Function for Text-to-Speech
+def text_to_speech(text):
+    tts = gTTS(text)
+    tts.save("response.mp3")
+    os.system("start response.mp3")  # For Windows, use "start", on Linux or macOS use "open"
+
+# Function for Speech-to-Text
+def speech_to_text():
+    recognizer = sr.Recognizer()
+    with sr.Microphone() as source:
+        print("Listening...")
+        audio = recognizer.listen(source)
+        query = recognizer.recognize_google(audio)
+        print(f"User: {query}")
+        return query
+
+# Function to generate responses using Hugging Face GPT model
+def generate_response(query):
+    generator = pipeline("text-generation", model="gpt2")
+    response = generator(query, max_length=50, num_return_sequences=1)
+    return response[0]['generated_text']
+
+# Main Streamlit function
 def main():
     st.title("Custom Multilingual Chatbot")
+    
+    mode = st.selectbox("Choose Mode", ["Text", "Voice"])
 
-    # User input for chatbot
-    user_input = st.text_input("Ask me anything:")
-
-    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 mode == "Text":
+        user_input = st.text_input("Ask me anything:")
+        if user_input:
+            url = "https://www.sbbusba.edu.pk/"  # Example URL, can be dynamically set by the user
+            web_content = scrape_website(url)
+            relevant_data = search(user_input, [web_content])
+            
+            response = generate_response(f"Based on the content of the website: {relevant_data[0]}")
+            st.write("Bot: " + response)
+            text_to_speech(response)  # Convert the text response to speech
+    
+    elif mode == "Voice":
+        if st.button("Start Listening"):
+            query = speech_to_text()  # Listen and convert to text
+            url = "https://www.sbbusba.edu.pk/"  # Example URL
+            web_content = scrape_website(url)
+            relevant_data = search(query, [web_content])
+            
+            response = generate_response(f"Based on the content of the website: {relevant_data[0]}")
+            st.write("Bot: " + response)
+            text_to_speech(response)  # Convert the text response to speech
 
 if __name__ == "__main__":
     main()