Update app.py

app.py

@@ -1,50 +1,21 @@
 import gradio as gr
-import threading
+import tensorflow
 from transformers import pipeline
 
 # Load the text summarization pipeline from Hugging Face Transformers
 summarizer = pipeline("summarization")
 
-input_text = ""
-summary = ""
-running = False
-lock = threading.Lock()
-
-def summarize_text():
-    global input_text, summary, running
-    while running:
-        with lock:
-            if input_text.strip():
-                summary = summarizer(input_text, max_length=100, min_length=20, do_sample=False)[0]['summary_text']
-
-# Start summarization in a separate thread
-def start_summarization():
-    global running
-    running = True
-    thread = threading.Thread(target=summarize_text)
-    thread.start()
-
-# Stop summarization
-def stop_summarization():
-    global running
-    running = False
-
-def update_input_text(new_input_text):
-    global input_text
-    input_text = new_input_text
-
-def get_summary():
-    with lock:
-        return summary
+def summarize_text(input_text):
+    summary = summarizer(input_text, max_length=100, min_length=20, do_sample=False)[0]['summary_text']
+    return summary
 
 # Interface for the Gradio app
 iface = gr.Interface(
-    fn=get_summary,
-    inputs=[gr.inputs.Textbox(lines=5, label="Input Text"), gr.inputs.Button("Start", onclick=start_summarization), gr.inputs.Button("Stop", onclick=stop_summarization)],
+    fn=summarize_text,
+    inputs=gr.inputs.Textbox(lines=5, label="Input Text"),
     outputs=gr.outputs.Textbox(label="Summary"),
-    live=True,
-    title="Interactive Text Summarizer",
-    description="Enter text, start the summarization, and stop it when you're satisfied.",
+    title="Text Summarizer",
+    description="Enter a piece of text, and the app will provide a summary.",
 )
 
 # Launch the Gradio app