Create app.py

app.py

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+import gradio as gr
+import PyPDF2
+import tempfile
+from transformers import pipeline
+
+# Step 1: Summarizer class using HuggingFace directly
+class TextSummarizer:
+    def __init__(self):
+        self.summarizer = pipeline(
+            "summarization",
+            model="facebook/bart-large-cnn"
+        )
+
+    def summarize_text(self, article_text, max_length=150, min_length=30):
+        # Truncate very long inputs
+        article_text = article_text.strip()
+        if len(article_text) > 1024:
+            article_text = article_text[:1024]
+
+        summary = self.summarizer(
+            article_text,
+            max_length=max_length,
+            min_length=min_length,
+            do_sample=False
+        )
+        return summary[0]['summary_text'] if summary else "No summary generated."
+
+# Step 2: PDF text extraction
+def pdf_to_text(pdf_file):
+    try:
+        with tempfile.NamedTemporaryFile(delete=False) as tmp:
+            tmp.write(pdf_file)
+            tmp.flush()
+            reader = PyPDF2.PdfReader(tmp.name)
+            text = "\n".join(page.extract_text() or "" for page in reader.pages)
+            return text.strip() if text.strip() else "No extractable text found in the PDF."
+    except Exception as e:
+        return f"Error reading PDF: {str(e)}"
+
+# Step 3: Summarization function for Gradio
+summarizer = TextSummarizer()
+
+def summarize_input(text, max_words):
+    if not text.strip():
+        return "Please enter or extract some text first."
+    try:
+        max_length = int(max_words)
+        min_length = max(30, max_length // 4)
+        return summarizer.summarize_text(text, max_length=max_length, min_length=min_length)
+    except Exception as e:
+        return f"Error during summarization: {str(e)}"
+
+# Step 4: Gradio UI setup
+with gr.Blocks() as demo:
+    gr.Markdown("## 📝 Text & PDF Summarizer")
+
+    with gr.Row():
+        text_input = gr.Textbox(label="Enter text to summarize", lines=15, placeholder="Paste your text here...")
+        pdf_file = gr.File(label="Or upload a PDF", file_types=[".pdf"], type="binary")
+
+    max_words = gr.Number(label="Max summary word count", value=150, precision=0)
+
+    with gr.Row():
+        convert_btn = gr.Button("Convert PDF to Text")
+        summarize_btn = gr.Button("Summarize Text")
+
+    output_text = gr.Textbox(label="Summary", lines=10)
+
+    convert_btn.click(fn=pdf_to_text, inputs=pdf_file, outputs=text_input)
+    summarize_btn.click(fn=summarize_input, inputs=[text_input, max_words], outputs=output_text)
+
+# Step 5: Launch the app
+if __name__ == "__main__":
+    demo.launch()