Create helper.py

helper.py

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+from PIL import Image, ImageDraw
+import io
+import base64
+import requests
+import json
+from typing import List, Dict, Any
+from openai import OpenAI
+
+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