File size: 3,971 Bytes
0451020 e530cc2 22f53cc e530cc2 22f53cc e530cc2 0451020 |
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 |
from PIL import Image, ImageDraw
import io
import base64
import requests
import json
from typing import List, Dict, Any
from openai import OpenAI
import streamlit as st
import streamlit.components.v1 as components
def custom_file_uploader():
uploader_html = """
<input type="file" id="fileUpload" accept=".png,.jpg,.jpeg" />
<script>
const fileInput = document.getElementById("fileUpload");
fileInput.addEventListener("change", function(event) {
if (fileInput.files.length > 0) {
const file = fileInput.files[0];
const reader = new FileReader();
reader.readAsDataURL(file);
reader.onload = function() {
const result = reader.result;
window.parent.postMessage({type: 'FILE_UPLOAD', file: result}, '*');
};
}
});
</script>
"""
st.markdown(uploader_html, unsafe_allow_html=True)
def resize_image(image: Image.Image) -> Image.Image:
new_height = int(image.height * 512 / image.width)
return image.resize((512, new_height))
def convert_image_to_base64(image: Image.Image) -> str:
buffered = io.BytesIO()
image.save(buffered, format="PNG")
return base64.b64encode(buffered.getvalue()).decode()
def post_request_and_parse_response(url: str, payload: Dict[str, Any]) -> Dict[str, Any]:
headers = {"Content-Type": "application/json"}
response = requests.post(url, json=payload, headers=headers)
byte_data = response.content
decoded_string = byte_data.decode("utf-8")
dict_data = json.loads(decoded_string)
return dict_data
def draw_bounding_boxes_for_textract(image: Image.Image, json_data: str) -> Image.Image:
draw = ImageDraw.Draw(image)
try:
data = json_data
blocks = json.loads(data['body']) if 'body' in data else None
except json.JSONDecodeError:
return image
if blocks is None:
return image
for item in blocks:
if 'BlockType' in item and item['BlockType'] in ['LINE', 'WORD']:
bbox = item['Geometry']['BoundingBox']
left, top, width, height = bbox['Left'], bbox['Top'], bbox['Width'], bbox['Height']
left_top = (left * image.width, top * image.height)
right_bottom = ((left + width) * image.width, (top + height) * image.height)
draw.rectangle([left_top, right_bottom], outline='red', width=2)
return image
def extract_text_from_textract_blocks(blocks: List[Dict[str, Any]]) -> str:
extracted_text = []
blocks = json.loads(blocks)
for block in blocks:
if isinstance(block, dict):
if block.get('BlockType') in ['LINE', 'WORD'] and 'Text' in block:
extracted_text.append(block['Text'])
return ' '.join(extracted_text)
class ChatGPTClient:
def __init__(self, api_key: str, protocol: str = "You are a helpful assistant.", body=None):
self.api_key = api_key
self.client = OpenAI(api_key=self.api_key)
self.protocol = protocol
self.body = body
self.history: List[Dict[str, str]] = [
{"role": "system", "content": self.protocol},
{"role": "user", "content": f"The content provided: {self.body}"}
]
def append_message(self, role: str, content: str) -> None:
if role in ["system", "user", "assistant"]:
self.history.append({"role": role, "content": content})
def generate_response(self, question: str) -> str:
try:
self.append_message("user", question)
response = self.client.chat.completions.create(
model="gpt-3.5-turbo",
messages=self.history
)
output = response.choices[0].message.content
self.append_message("assistant", output)
except Exception as e:
output = "Sorry, I couldn't get an answer for that."
return output
|