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35c1873 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 | import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import os
from huggingface_hub import login
model_name_tinyllama = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
tokenizer_tinyllama = AutoTokenizer.from_pretrained(model_name_tinyllama)
model_tinyllama = AutoModelForCausalLM.from_pretrained(model_name_tinyllama,torch_dtype=torch.float32,device_map={"": "cpu"})
def summarize_tinyllama(article):
# For causal models like TinyLlama, summarization isn't a direct task like with encoder-decoder models.
# We can prompt it to continue a summary.
prompt="Summarize the following article clearly and concisely:"
input_text = f"{prompt}\n{article}\nSummary:"
inputs = tokenizer_tinyllama(input_text, return_tensors="pt", max_length=1024, truncation=True)
# Generate tokens - the model will try to complete the input prompt.
# We need to adjust generation parameters for open-ended generation.
# max_new_tokens controls how much new text is generated after the prompt.
outputs = model_tinyllama.generate(
inputs["input_ids"],
attention_mask=inputs["attention_mask"],
max_new_tokens=500, # Generate up to 300 new tokens for the summary
do_sample=True, # Don't sample, use greedy decoding
temperature=0.7,
min_new_tokens=150,
top_p=0.9,
pad_token_id=tokenizer_tinyllama.eos_token_id, # Pad with EOS token if needed
)
# Decode the entire output sequence.
generated_text = tokenizer_tinyllama.decode(outputs[0], skip_special_tokens=True)
# The generated text will include the original prompt. We need to extract the summary part.
# This is a simple approach, more sophisticated parsing might be needed depending on prompt and output.
summary_start_index = generated_text.find("Summary:") + len("Summary:")
summary = generated_text[summary_start_index:].strip()
return summary
def answer_question_tinyllama(article, question):
# Formulate the prompt to guide the TinyLlama model to answer the question based on the article.
# We ask the model to act as an AI answering a question based on the provided text.
input_text = f"From this Article: {article}\n\n Answer the below Question: {question}\n\nAnswer:"
# Tokenize the input text
# Truncate if the combined article and question is too long
inputs = tokenizer_tinyllama(input_text, return_tensors="pt", max_length=1024, truncation=True)
# Generate the answer using the model.
# We use generate with parameters suitable for generating a concise answer.
outputs = model_tinyllama.generate(
inputs["input_ids"],
attention_mask=inputs["attention_mask"],
max_new_tokens=500, # Generate up to 100 new tokens for the answer
do_sample=True, # Use sampling to potentially get more varied answers
temperature=0.7, # Control randomness
top_p=0.9, # Nucleus sampling
pad_token_id=tokenizer_tinyllama.eos_token_id, # Pad with EOS token if needed
)
# Decode the generated sequence
generated_text = tokenizer_tinyllama.decode(outputs[0], skip_special_tokens=True)
# The generated text will include the original prompt. We need to extract the answer part.
# This is a simple approach, more sophisticated parsing might be needed depending on prompt and output.
answer_start_index = generated_text.find("Answer:") + len("Answer:")
answer = generated_text[answer_start_index:].strip()
# Basic cleanup: remove potential repetition of the question or prompt in the answer
if answer.startswith(question):
answer = answer[len(question):].strip()
return answer
st.title("Smart Article Insights Generator")
st.markdown("Summarize an article or ask a question about it.")
mode = st.radio("Select Mode", ["Summarize", "Answer Question"])
article_input = st.text_area("Article Text", height=300, placeholder="Paste the article here...")
question_input = None
if mode == "Answer Question":
question_input = st.text_input("Question", placeholder="Enter your question here...")
if st.button("Process"):
if mode == "Summarize":
if article_input:
with st.spinner("Generating summary..."):
output = summarize_tinyllama(article_input)
st.subheader("Summary")
st.write(output)
else:
st.warning("Please provide an article to summarize.")
elif mode == "Answer Question":
if article_input and question_input:
with st.spinner("Generating answer..."):
output = answer_question_tinyllama(article_input, question_input)
st.subheader("Answer")
st.write(output)
elif not article_input:
st.warning("Please provide an article to answer the question from.")
elif not question_input:
st.warning("Please provide a question to answer.")
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