Delete text_summarizer.py

text_summarizer.py

@@ -1,95 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-from newspaper import Article
-import fitz  # PyMuPDF
-from summarizer import Summarizer
-
-# --------- UTILITY FUNCTIONS ---------
-
-def extract_text_from_pdf(pdf_file):
-    doc = fitz.open(stream=pdf_file, filetype="pdf")
-    text = ""
-    for page in doc:
-        text += page.get_text()
-    return text
-
-def extract_text_from_url(url):
-    article = Article(url)
-    article.download()
-    article.parse()
-    return article.text
-
-abstractive_summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
-extractive_summarizer = Summarizer()
-
-def generate_abstractive_summary(text, max_length=130, min_length=30):
-    summary = abstractive_summarizer(text, max_length=max_length, min_length=min_length, do_sample=False)
-    return summary[0]['summary_text']
-
-def generate_extractive_summary(text, ratio=0.3):
-    return extractive_summarizer(text, ratio=ratio)
-
-def summarize_text(source_type, text, pdf, url, max_length, min_length, ratio):
-    input_text = ""
-
-    try:
-        if source_type == "Text" and text:
-            input_text = text
-        elif source_type == "PDF" and pdf is not None:
-            input_text = extract_text_from_pdf(pdf)
-        elif source_type == "URL" and url:
-            input_text = extract_text_from_url(url)
-        else:
-            return "❗Please provide a valid input.", ""
-
-        if len(input_text.strip()) == 0:
-            return "❗Input is empty after extraction.", ""
-
-        # Bart/T5 models handle ~1024 tokens (~2000 characters)
-        input_text = input_text[:2000]
-
-        abstractive = generate_abstractive_summary(input_text, max_length, min_length)
-        extractive = generate_extractive_summary(input_text, ratio)
-
-        return abstractive, extractive
-
-    except Exception as e:
-        return f"⚠️ Error: {str(e)}", ""
-
-# --------- GRADIO UI ---------
-
-with gr.Blocks() as demo:
-    gr.Markdown("## 🧠 AI Text Summarizer\nChoose input type and get both **abstractive** and **extractive** summaries.")
-
-    source_type = gr.Radio(["Text", "PDF", "URL"], label="Select Input Source")
-
-    text_input = gr.Textbox(lines=8, label="Enter Text", visible=False)
-    pdf_input = gr.File(label="Upload PDF", type="binary", visible=False)
-    url_input = gr.Textbox(label="Enter URL", visible=False)
-
-    max_length = gr.Slider(50, 300, step=10, value=130, label="Max Length (Abstractive)")
-    min_length = gr.Slider(20, 100, step=10, value=30, label="Min Length (Abstractive)")
-    ratio = gr.Slider(0.1, 1.0, step=0.1, value=0.3, label="Summary Ratio (Extractive)")
-
-    btn = gr.Button("Generate Summaries")
-
-    output_ab = gr.Textbox(label="Abstractive Summary")
-    output_ex = gr.Textbox(label="Extractive Summary")
-
-    def toggle_inputs(src):
-        return {
-            text_input: gr.update(visible=(src == "Text")),
-            pdf_input: gr.update(visible=(src == "PDF")),
-            url_input: gr.update(visible=(src == "URL"))
-        }
-
-    source_type.change(fn=toggle_inputs, inputs=source_type, outputs=[text_input, pdf_input, url_input])
-
-    btn.click(
-        summarize_text,
-        inputs=[source_type, text_input, pdf_input, url_input, max_length, min_length, ratio],
-        outputs=[output_ab, output_ex]
-    )
-
-if __name__ == "__main__":
-    demo.launch()