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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+UI.JPG filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,106 @@
+# 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": 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()

requirements.txt

@@ -0,0 +1,6 @@
+gradio
+PyPDF2
+langchain
+langchain-huggingface
+transformers
+torch