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| # Running on local URL: http://127.0.0.1:7860 | |
| import gradio as gr # Gradio: for creating web-based user interfaces | |
| import PyPDF2 # PyPDF2: for reading PDF files | |
| import tempfile # tempfile: to safely handle temporary files | |
| from langchain.prompts import PromptTemplate # LangChain: for managing prompt templates | |
| from langchain_huggingface.llms import HuggingFacePipeline # LangChain integration with HuggingFace models | |
| # Define a summarization class | |
| class TextSummarizer: | |
| def __init__(self): | |
| # Define the model to use for summarization | |
| self.model_id = "facebook/bart-large-cnn" | |
| def summarize_text(self, article_text, max_length=150, min_length=30): | |
| # Load a summarization pipeline with custom length settings | |
| llm = HuggingFacePipeline.from_model_id( | |
| model_id=self.model_id, | |
| task="summarization", | |
| pipeline_kwargs={ | |
| #"max_length": 250, | |
| #"do_sample": True, | |
| #"temperature": 0.7, # More creative | |
| #"top_k": 50, # Limit to top 50 tokens | |
| #"top_p": 0.95 # Use nucleus sampling | |
| "max_length": max_length, | |
| "min_length": min_length, | |
| "do_sample": False # Deterministic output | |
| } | |
| ) | |
| # Create a basic prompt template that just passes the text | |
| prompt = PromptTemplate(input_variables=["document"], template="""{document}""") | |
| # Format the article text into the prompt | |
| prompt_input = prompt.format(document=article_text) | |
| # Generate the summary using the model | |
| summary = llm.__call__(prompt_input) | |
| # If the model returns a list of summaries, extract the actual summary text | |
| if isinstance(summary, list): | |
| return summary[0]['summary_text'] if 'summary_text' in summary[0] else str(summary[0]) | |
| return str(summary) # Fallback for other formats | |
| # Function to extract text from an uploaded PDF | |
| def pdf_to_text(pdf_file): | |
| try: | |
| # Create a temporary file to write the uploaded PDF bytes | |
| with tempfile.NamedTemporaryFile(delete=False) as tmp: | |
| tmp.write(pdf_file) # Write raw bytes directly | |
| tmp.flush() # Make sure data is written to disk | |
| # Use PyPDF2 to read and extract text | |
| reader = PyPDF2.PdfReader(tmp.name) | |
| text = "\n".join(page.extract_text() or "" for page in reader.pages) | |
| # Return cleaned-up text or a message if extraction fails | |
| 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)}" # Return readable error message | |
| # Instantiate the summarizer class | |
| summarizer = TextSummarizer() | |
| # Summarize input with user-defined maximum length | |
| def summarize_input(text, max_words): | |
| if not text.strip(): | |
| return "Please enter or extract some text first." | |
| try: | |
| # Convert max_words input to integer | |
| max_length = int(max_words) | |
| # Set a safe minimum length for quality summaries | |
| min_length = max(30, max_length // 4) | |
| # Generate the summary | |
| return summarizer.summarize_text(text, max_length=max_length, min_length=min_length) | |
| except Exception as e: | |
| return f"Error during summarization: {str(e)}" | |
| # Build the Gradio UI | |
| with gr.Blocks() as demo: | |
| gr.Markdown("## 📝 Text & PDF Summarizer with Length Control") | |
| with gr.Row(): | |
| # Text input for manually entering article | |
| text_input = gr.Textbox(label="Enter article text", lines=15, placeholder="Paste your article here...") | |
| # Upload input for PDF files | |
| pdf_file = gr.File(label="Or upload PDF", file_types=[".pdf"], type="binary") | |
| # User input for controlling max summary length | |
| max_words = gr.Number(label="Max summary word count", value=150, precision=0) | |
| with gr.Row(): | |
| # Button to convert PDF to text | |
| convert_btn = gr.Button("Convert PDF to Text") | |
| # Button to generate the summary | |
| summary_btn = gr.Button("Summarize Text") | |
| # Textbox to display the summary output | |
| output_text = gr.Textbox(label="Summary", lines=10) | |
| # Link buttons to their respective functions | |
| convert_btn.click(fn=pdf_to_text, inputs=pdf_file, outputs=text_input) | |
| summary_btn.click(fn=summarize_input, inputs=[text_input, max_words], outputs=output_text) | |
| # Launch the app if run directly | |
| if __name__ == "__main__": | |
| demo.launch() | |