Spaces:
Sleeping
Sleeping
File size: 4,183 Bytes
79b4e95 0efbb5e 79b4e95 0efbb5e 79b4e95 0efbb5e 79b4e95 0efbb5e 79b4e95 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 |
import streamlit as st
import pdfplumber
from PIL import Image
import pytesseract
#from transformers import pipeline
import io
import os
from dotenv import load_dotenv
from groqSummarizer import GroqSummarizer
# SwedishBeagle-dare
from transformers import AutoTokenizer
import transformers
import torch
class Summarizer:
def __init__(self, model = "groq"):
self.model = model
def run_app(self):
uploaded_file = st.file_uploader("Upload an Image or PDF", type=["jpg", "jpeg", "png", "pdf"])
if uploaded_file is not None:
if uploaded_file.type == "application/pdf":
with st.spinner("Extracting text from PDF..."):
text = self.extract_text_from_pdf(uploaded_file)
else:
image = Image.open(uploaded_file)
with st.spinner("Extracting text from image..."):
text = self.extract_text_from_image(image)
if text:
with st.spinner("Summarizing text..."):
summary = self.summarize_using_groq(text)
st.subheader("Summary")
st.write(summary)
st.subheader("Extracted Text")
st.write(text)
# Function to extract text from an image
def extract_text_from_image(self, image):
text = pytesseract.image_to_string(image)
return text
# Function to extract text from a PDF
def extract_text_from_pdf(self, pdf):
text = ""
with pdfplumber.open(pdf) as pdf_file:
for page in pdf_file.pages:
text += page.extract_text()
return text
def shorten_text(self, text, max_tokens):
tokens = text.split(" ")
if len(tokens) > max_tokens:
tokens = tokens[:max_tokens]
text = " ".join(tokens)
print("Shortened text to " + str(max_tokens) + " tokens")
return text
def summarize_using_groq(self, text):
# Decrease the number of tokens if the response is 429, i.e. too many tokens in the request
#
# https://context.ai/compare/llama3-70b-instruct-v1/gpt-4
# ^^ Säger att max tokens är 8000, men efter tester så verkar det vara
# närmare 2000 om man räknar att tokens är separerade med blanksteg.
# (Detta är inte ett helt korrekt sätt att räkna det)
# max_tokens = 8000
max_tokens = 2000
while True:
try:
gs = GroqSummarizer()
return gs.summarize(text)
except Exception as e:
if e.response.status_code == 429:
text = self.shorten_text(text, max_tokens)
max_tokens = int(max_tokens * 0.9)
else:
return "Error: " + str(e)
def summarize_using_swedishbeagle(self, text):
# https://huggingface.co/FredrikBL/SwedishBeagle-dare
model = "FredrikBL/SwedishBeagle-dare"
messages = [
{
"role": "system",
"content": "You summarize texts that the users sends"
},
{
"role": "user",
"content": text
}
]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
return outputs[0]["generated_text"]
def summarize(self, text):
if(self.model == "groq"):
return self.summarize_using_groq(text)
elif(self.model == "SwedishBeagle-dare"):
return self.summarize_using_swedishbeagle(text)
# Streamlit app
def main():
# Models:
# - groq
# - SwedishBeagle-dare
summarizer = Summarizer(model = "groq")
summarizer.run_app()
if __name__ == "__main__":
main() |