Create app.py

app.py

@@ -0,0 +1,45 @@
+import gradio as gr
+from transformers import BartForConditionalGeneration, BartTokenizer
+
+# Load the model
+model_name = "philschmid/bart-large-cnn-samsum"
+tokenizer = BartTokenizer.from_pretrained(model_name)
+model = BartForConditionalGeneration.from_pretrained(model_name)
+
+# Summary length options
+length_options = {
+    "Short": (30, 50),
+    "Medium": (50, 100),
+    "Long": (100, 150)
+}
+
+# Function to process summarization
+def summarize_text(text, summary_length):
+    min_len, max_len = length_options.get(summary_length, (50, 100))  # Default to Medium
+
+    inputs = tokenizer(text, return_tensors="pt", max_length=1024, truncation=True)
+    summary_ids = model.generate(
+        inputs["input_ids"], 
+        max_length=max_len, 
+        min_length=min_len, 
+        length_penalty=1.0, 
+        num_beams=6, 
+        repetition_penalty=1.2
+    )
+
+    return tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("# AI-Powered Summarizer ✨")
+    
+    with gr.Row():
+        text_input = gr.Textbox(label="Enter Text to Summarize", lines=8, placeholder="Paste your text here...")
+    
+    summary_length = gr.Radio(["Short", "Medium", "Long"], value="Medium", label="Summary Length")
+
+    summarize_button = gr.Button("Summarize")
+
+    output_text = gr.Textbox(label="Summary", lines=6)
+
+    summarize_button.click(summarize