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import gradio as gr
from PyPDF2 import PdfReader
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
# Load summarizer model (LaMini-Flan-T5)
summarizer_tokenizer = AutoTokenizer.from_pretrained("MBZUAI/LaMini-Flan-T5-248M")
summarizer_model = AutoModelForSeq2SeqLM.from_pretrained("MBZUAI/LaMini-Flan-T5-248M")
# Load translators
translator_hi = pipeline("translation", model="Helsinki-NLP/opus-mt-en-hi")
translator_te = pipeline("translation", model="Helsinki-NLP/opus-mt-en-mul")
# Extract text from PDF
def extract_text_from_pdf(file):
reader = PdfReader(file)
text = ""
for page in reader.pages:
text += page.extract_text()
return text
# Summarize based on doc type
def summarize_text(text, doc_type):
prompt = f"Summarize this {doc_type} document clearly:\n{text}\nSummary:"
inputs = summarizer_tokenizer(prompt, return_tensors="pt", truncation=True, max_length=1024)
outputs = summarizer_model.generate(**inputs, max_length=300, num_beams=4, early_stopping=True)
return summarizer_tokenizer.decode(outputs[0], skip_special_tokens=True)
# Translate summary
def translate_summary(summary, lang):
if lang == "hindi":
return translator_hi(summary)[0]["translation_text"]
elif lang == "telugu":
return translator_te(summary)[0]["translation_text"]
else:
return summary # English or unsupported
# Main processing logic
def process(file, lang, doc_type):
text = extract_text_from_pdf(file)
if not text.strip():
return "Error: PDF has no extractable text."
summary = summarize_text(text, doc_type)
return translate_summary(summary, lang)
# Gradio UI
with gr.Blocks() as app:
gr.Markdown("## Multilingual AI Document Summarizer")
gr.Markdown("Upload a document and get summaries in multiple languages using mT5.")
file_input = gr.File(label="Upload PDF")
with gr.Row():
language_input = gr.Dropdown(
label="Select Language",
choices=["english", "hindi", "telugu"],
value="english"
)
type_input = gr.Dropdown(
label="Select Document Type",
choices=["legal", "medical", "general"],
value="general"
)
output = gr.Textbox(label="Summary Output", lines=10)
with gr.Row():
clear = gr.Button("Clear")
submit = gr.Button("Submit")
submit.click(fn=process, inputs=[file_input, language_input, type_input], outputs=output)
clear.click(lambda: "", inputs=[], outputs=output)
app.launch()
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