File Updated

app.py

@@ -0,0 +1,40 @@
+import torch
+import gradio as gr
+from transformers import pipeline
+
+# Initialize the summarization pipeline
+Text_summary = pipeline("summarization", model="facebook/bart-large-cnn", torch_dtype=torch.bfloat16)
+
+# Define a function to estimate token count from word count
+def estimate_tokens(word_count):
+    # Approximate tokens as 1.5 times the word count
+    return int(word_count * 1.5)
+
+# Define the summarization function
+def summary(input, word_count):
+    # Convert word count to token count
+    max_length = estimate_tokens(word_count)
+    min_length = max(10, max_length // 2)  # Set a reasonable minimum length
+    output = Text_summary(input, max_length=max_length, min_length=min_length)
+    return output[0]['summary_text']
+
+# Close any existing Gradio instances
+gr.close_all()
+
+# Set up the Gradio interface
+Demo = gr.Interface(
+    fn=summary,
+    inputs=[
+        gr.Textbox(label="Input Text To Summarize", lines=20),
+        gr.Slider(
+            label="Summary Length (Words Approx.)", 
+            minimum=50, maximum=300, step=10, value=130
+        )
+    ],
+    outputs=[gr.Textbox(label="Summarized Text", lines=4)],
+    title="Text_Summarize_App",
+    description="THIS APPLICATION WILL BE USED TO SUMMARIZE THE TEXT"
+)
+
+# Launch the app with a public link
+Demo.launch(share=True)