Update app.py

app.py

@@ -1,23 +1,20 @@
 import gradio as gr
 from langchain.chains import LLMChain
 from langchain.prompts import PromptTemplate
-from langchain.document_loaders import WebBaseLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.llms import HuggingFacePipeline
 from transformers import pipeline
-import tempfile
-import os
 from bs4 import BeautifulSoup
 import requests
-import pyttsx3
+from TTS.api import TTS
+import tempfile
 
-# CPU-friendly summarization LLM
+# Setup summarization LLM
 summary_pipe = pipeline("text2text-generation", model="google/flan-t5-base", device=-1)
 llm = HuggingFacePipeline(pipeline=summary_pipe)
 
-# Summarization prompt
+# Prompt for more engaging summary
 summary_prompt = PromptTemplate.from_template("""
-Summarize the following article content in a clear, concise, and emotionally engaging manner as if you're speaking to a curious listener:
+Summarize the following article content in a clear, warm, and motivational tone like a preacher speaking to an audience:
 
 {text}
 
@@ -26,6 +23,9 @@ Summary:
 
 summary_chain = LLMChain(llm=llm, prompt=summary_prompt)
 
+# TTS model setup (multi-lingual, expressive)
+tts_model = TTS(model_name="tts_models/multilingual/multi-dataset/your_tts", progress_bar=False, gpu=False)
+
 def extract_main_content(url):
     try:
         response = requests.get(url, timeout=10)
@@ -42,21 +42,8 @@ def extract_main_content(url):
 
 def generate_human_like_audio(text):
     try:
-        engine = pyttsx3.init()
-        engine.setProperty('rate', 150)  # slower pace
-        engine.setProperty('volume', 1.0)
-        voices = engine.getProperty('voices')
-
-        # Choose a more natural voice if available (optional: pick female)
-        for voice in voices:
-            if 'female' in voice.name.lower():
-                engine.setProperty('voice', voice.id)
-                break
-
-        temp_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
-        engine.save_to_file(text, temp_path.name)
-        engine.runAndWait()
-
+        temp_path = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
+        tts_model.tts_to_file(text=text, file_path=temp_path.name)
         return temp_path.name
     except Exception as e:
         return None
@@ -74,7 +61,6 @@ def url_to_audio_summary(url):
             return summary, None
 
         return summary, audio_path
-
     except Exception as e:
         return f"Error: {str(e)}", None
 
@@ -83,10 +69,10 @@ iface = gr.Interface(
     inputs=gr.Textbox(label="Article URL", placeholder="Paste a news/blog URL here..."),
     outputs=[
         gr.Textbox(label="Summary"),
-        gr.Audio(label="Audio Summary")
+        gr.Audio(label="Preacher-style Audio Summary")
     ],
-    title="URL to Audio Summary Agent",
-    description="Summarizes only the article content from a URL and gives a more human-like audio summary using pyttsx3. CPU-only."
+    title="Preaching-Style URL to Audio Agent",
+    description="Summarizes article content and reads it aloud in a warm, preacher-style voice using YourTTS. CPU-only."
 )
 
 if __name__ == "__main__":