Update app.py

app.py

@@ -1,24 +1,51 @@
 import gradio as gr
-import tensorflow
+import threading
 from transformers import pipeline
 
 # Load the text summarization pipeline from Hugging Face Transformers
 summarizer = pipeline("summarization")
 
-def summarize_text(input_text):
-    summary = summarizer(input_text, max_length=100, min_length=20, do_sample=False)[0]['summary_text']
-    return summary
+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
 
 # Interface for the Gradio app
 iface = gr.Interface(
-    fn=summarize_text,
-    inputs=gr.inputs.Textbox(lines=5, label="Input Text"),
+    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)],
     outputs=gr.outputs.Textbox(label="Summary"),
-    title="Text Summarizer",
-    description="Enter a piece of text, and the app will provide a summary.",
+    live=True,
+    title="Interactive Text Summarizer",
+    description="Enter text, start the summarization, and stop it when you're satisfied.",
 )
 
 # Launch the Gradio app
 iface.launch()
-
-iface.close()