Update app.py

app.py

@@ -7,7 +7,7 @@ import requests
 from bs4 import BeautifulSoup
 import concurrent.futures
 import time
-import pyttsx3
+from gtts import gTTS
 import io
 import base64
 
@@ -17,9 +17,6 @@ logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(
 summarizer = pipeline("summarization", model="t5-small", tokenizer="t5-small")
 tokenizer = AutoTokenizer.from_pretrained("t5-small")
 
-# Initialize pyttsx3
-engine = pyttsx3.init()
-
 def fetch_content_from_url(url):
     try:
         response = requests.get(url, timeout=10)
@@ -35,93 +32,4 @@ def chunk_text(text, max_chunk_size=200):
     words = text.split()[:1000]  # Limit to first 1000 words
     chunks = []
     current_chunk = []
-    current_size = 0
-    for word in words:
-        if current_size + len(word) > max_chunk_size:
-            chunks.append(' '.join(current_chunk))
-            current_chunk = [word]
-            current_size = len(word)
-        else:
-            current_chunk.append(word)
-            current_size += len(word) + 1
-    if current_chunk:
-        chunks.append(' '.join(current_chunk))
-    return chunks
-
-def summarize_chunk(chunk):
-    try:
-        summary = summarizer(chunk, max_length=50, min_length=10, do_sample=False)[0]['summary_text']
-        return summary
-    except Exception as e:
-        logging.error(f"Error summarizing chunk: {str(e)}")
-        return ""
-
-def text_to_speech(text):
-    try:
-        engine.save_to_file(text, 'summary.mp3')
-        engine.runAndWait()
-        with open("summary.mp3", "rb") as audio_file:
-            audio_base64 = base64.b64encode(audio_file.read()).decode('utf-8')
-        return f'data:audio/mp3;base64,{audio_base64}'
-    except Exception as e:
-        logging.error(f"Error in text-to-speech conversion: {str(e)}")
-        return None
-
-def summarize_text(input_text, is_url):
-    try:
-        start_time = time.time()
-        if is_url:
-            content = fetch_content_from_url(input_text)
-            if content.startswith("Error"):
-                return content, None
-        else:
-            content = input_text[:10000]  # Limit direct input to 10000 characters
-
-        chunks = chunk_text(content)
-        with concurrent.futures.ThreadPoolExecutor() as executor:
-            summaries = list(executor.map(summarize_chunk, chunks))
-
-        full_summary = " ".join(summaries)
-        
-        processing_time = time.time() - start_time
-        summary_with_time = f"Summary (processed in {processing_time:.2f} seconds):\n\n{full_summary}"
-        
-        audio = text_to_speech(full_summary)
-        
-        return summary_with_time, audio
-    except Exception as e:
-        error_msg = f"An error occurred: {str(e)}\n\nTraceback:\n{traceback.format_exc()}"
-        logging.error(error_msg)
-        return error_msg, None
-
-def debug_info():
-    return (
-        f"Python version: {sys.version}\n"
-        f"Summarizer model: T5-small\n"
-        f"TTS Engine: pyttsx3\n"
-        f"Current working directory: {os.getcwd()}\n"
-        f"Contents of current directory: {os.listdir()}"
-    )
-
-iface = gr.Interface(
-    fn=summarize_text,
-    inputs=[
-        gr.Textbox(lines=5, label="Input Text or URL"),
-        gr.Checkbox(label="Is URL?")
-    ],
-    outputs=[
-        gr.Textbox(label="Summary", lines=10),
-        gr.Audio(label="Audio Summary")
-    ],
-    title="Quick Text Summarizer with Speech",
-    description="Enter text or a URL to summarize. Long texts will be truncated for quick processing."
-)
-
-debug_interface = gr.Interface(
-    fn=debug_info,
-    inputs=None,
-    outputs="text",
-    title="Debug Information"
-)
-
-gr.TabbedInterface([iface, debug_interface], ["Summarizer", "Debug Info"]).launch()
+    current_size