Upload app (1).py

app (1).py

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+import gradio as gr
+from transformers import BartForConditionalGeneration, BartTokenizer
+from PyPDF2 import PdfFileReader
+import torch
+
+# Loading BART 
+model_name = "facebook/bart-large-cnn"
+tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = BartForConditionalGeneration.from_pretrained(model_name).to(device)
+
+# Function to Calculate summary lengths 
+def calc_summary_lengths(text_length):
+    short_min = int(0.10 * text_length)
+    medium_min = short_max_ =  int(0.15 * text_length)
+    medium_max = long_min =  int(0.20 * text_length)
+    long_max = int(0.30 * text_length)
+    return {
+        "Short": (short_min, short_max_),
+        "Medium": (medium_min, medium_max),
+        "Long": (long_min, long_max)
+    }
+
+# Function to summarize text
+def summarize_text(pdf_file, summary_length):
+    try:
+        text = ""
+        with open(pdf_file.name, "rb") as f:
+            reader = PdfFileReader(f)
+            for page in range(reader.numPages):
+                text += reader.getPage(page).extractText()
+
+        text = " ".join(text.split())
+        text_length = len(text.split())
+
+        summary_range = calc_summary_lengths(text_length)
+        min_length, max_length = summary_range[summary_length]
+
+        # Summary Generation
+        inputs = tokenizer.encode(text, max_length=1024, return_tensors='pt', truncation=True).to(device)
+        summary_ids = model.generate(inputs, num_beams=4, min_length=min_length, max_length=max_length, early_stopping=True)
+        summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+        return summary
+    except Exception as e:
+        return f"Error: {str(e)} \nPlease check file size and type!"
+
+
+
+input_component = gr.File(label="Upload PDF file")
+output_component = gr.Textbox(label="Summarized Text")
+summary_length_component = gr.Dropdown(label="Summary Length", choices=["Short", "Medium", "Long"])
+
+title = "PDF Text Summarizer (BART)"
+description = "<h2>Upload a PDF file and select the desired summary length.</h2>"
+
+InterFace = gr.Interface(fn=summarize_text, inputs=[input_component, summary_length_component], outputs=output_component, title=title, description=description)
+InterFace.launch()