Upload app.py

app.py

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+import gradio as gr
+import pdfplumber
+from transformers import pipeline
+
+# Load HuggingFace free models
+summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
+qa_model = pipeline("question-answering", model="google/flan-t5-small")
+
+def extract_text_from_pdf(pdf_file):
+    text = ""
+    with pdfplumber.open(pdf_file.name) as pdf:
+        for page in pdf.pages:
+            text += page.extract_text() + "\n"
+    return text
+
+def summarize_pdf(pdf_file):
+    text = extract_text_from_pdf(pdf_file)
+    summary = summarizer(text, max_length=200, min_length=50, do_sample=False)
+    return summary[0]['summary_text']
+
+def generate_qa(pdf_file, question):
+    text = extract_text_from_pdf(pdf_file)
+    answer = qa_model(question=question, context=text)
+    return answer['answer']
+
+with gr.Blocks() as demo:
+    gr.Markdown("# PDF Summarizer + Q&A (Free)")
+
+    with gr.Tab("Summarize PDF"):
+        pdf_input = gr.File(label="Upload PDF")
+        summary_output = gr.Textbox(label="Summary", lines=10)
+        summarize_btn = gr.Button("Generate Summary")
+        summarize_btn.click(summarize_pdf, inputs=pdf_input, outputs=summary_output)
+
+    with gr.Tab("PDF Q&A"):
+        pdf_input_qa = gr.File(label="Upload PDF")
+        question_input = gr.Textbox(label="Ask a Question")
+        answer_output = gr.Textbox(label="Answer", lines=5)
+        qa_btn = gr.Button("Get Answer")
+        qa_btn.click(generate_qa, inputs=[pdf_input_qa, question_input], outputs=answer_output)
+
+demo.launch()