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import streamlit as st
from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer
from TTS.api import TTS
from tempfile import NamedTemporaryFile
import os
# Initialize models
@st.cache_resource
def load_models():
summarizer_model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-base")
summarizer_tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-base")
question_generator = pipeline("text2text-generation", model="valhalla/t5-small-qg-hl")
qa_model = pipeline("question-answering", model="deepset/roberta-base-squad2")
tts = TTS(model_name="tts_models/en/ljspeech/tacotron2-DDC", progress_bar=False, gpu=False)
return summarizer_model, summarizer_tokenizer, question_generator, qa_model, tts
summarizer_model, summarizer_tokenizer, question_generator, qa_model, tts = load_models()
def summarize_document(text):
input_ids = summarizer_tokenizer.encode(text, return_tensors="pt", max_length=512, truncation=True)
summary_ids = summarizer_model.generate(input_ids, max_length=150, min_length=30, length_penalty=2.0, num_beams=4, early_stopping=True)
return summarizer_tokenizer.decode(summary_ids[0], skip_special_tokens=True)
def generate_questions(summary):
questions = question_generator(summary)
return [q["generated_text"] for q in questions]
def generate_audio(text, voice_gender):
voice = "ljspeech" # Use default LJSpeech voice for both genders
audio_file = NamedTemporaryFile(delete=False, suffix=".wav")
tts.tts_to_file(text=text, file_path=audio_file.name)
return audio_file.name
def app():
st.title("Interactive Document Summarizer")
uploaded_file = st.file_uploader("Upload a document", type=["txt", "pdf", "docx"])
if uploaded_file:
raw_text = uploaded_file.read().decode("utf-8")
st.write("Processing document...")
# Summarize
summary = summarize_document(raw_text)
st.write("Summary Generated:")
st.write(summary)
# Generate dialogue
questions = generate_questions(summary)
dialogue = []
for idx, question in enumerate(questions):
dialogue.append({"persona": "male" if idx % 2 == 0 else "female", "text": question})
# Interactive simulation
st.write("Simulating Conversation:")
for item in dialogue:
st.write(f"{item['persona'].capitalize()} says: {item['text']}")
audio_path = generate_audio(item["text"], item["persona"])
st.audio(audio_path, format="audio/wav")
if __name__ == "__main__":
app()
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