Delete openai

openai/oai_extractor.py

@@ -1,78 +0,0 @@
-from typing import List, Union, Optional
-from indexify_extractor_sdk import Content, Extractor, Feature
-from pydantic import BaseModel, Field
-import os
-import base64
-from openai import OpenAI
-from pdf2image import convert_from_path
-import tempfile
-import mimetypes
-
-class OAIExtractorConfig(BaseModel):
-    model_name: Optional[str] = Field(default='gpt-3.5-turbo')
-    key: Optional[str] = Field(default=None)
-    prompt: str = Field(default='You are a helpful assistant.')
-    query: Optional[str] = Field(default=None)
-
-class OAIExtractor(Extractor):
-    name = "tensorlake/openai"
-    description = "An extractor that let's you use LLMs from OpenAI."
-    system_dependencies = []
-    input_mime_types = ["text/plain", "application/pdf", "image/jpeg", "image/png"]
-
-    def __init__(self):
-        super(OAIExtractor, self).__init__()
-
-    def extract(self, content: Content, params: OAIExtractorConfig) -> List[Union[Feature, Content]]:
-        contents = []
-        model_name = params.model_name
-        key = params.key
-        prompt = params.prompt
-        query = params.query
-
-        if content.content_type in ["application/pdf"]:
-            with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
-                temp_file.write(content.data)
-                file_path = temp_file.name
-                images = convert_from_path(file_path)
-                image_files = []
-                for i in range(len(images)):
-                    with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_image_file:
-                        images[i].save(temp_image_file.name, 'JPEG')
-                        image_files.append(temp_image_file.name)
-        elif content.content_type in ["image/jpeg", "image/png"]:
-            image_files = []
-            suffix = mimetypes.guess_extension(content.content_type)
-            with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as temp_image_file:
-                temp_image_file.write(content.data)
-                file_path = temp_image_file.name
-                image_files.append(file_path)
-        else:
-            text = content.data.decode("utf-8")
-            if query is None:
-                query = text
-            file_path = None
-        
-        def encode_image(image_path):
-            with open(image_path, "rb") as image_file:
-                return base64.b64encode(image_file.read()).decode('utf-8')
-
-        if ('OPENAI_API_KEY' not in os.environ) and (key is None):
-            response_content = "The OPENAI_API_KEY environment variable is not present."
-        else:
-            if ('OPENAI_API_KEY' in os.environ) and (key is None):
-                client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
-            else:
-                client = OpenAI(api_key=key)
-            if file_path:
-                encoded_images = [encode_image(image_path) for image_path in image_files]
-                messages_content = [ { "role": "user", "content": [ { "type": "text", "text": prompt, } ] + [ { "type": "image_url", "image_url": { "url": f"data:image/jpeg;base64,{encoded_image}", }, } for encoded_image in encoded_images ], } ]
-            else:
-                messages_content = [ {"role": "system", "content": prompt}, {"role": "user", "content": query} ]
-
-            response = client.chat.completions.create( model=model_name, messages=messages_content )
-            response_content = response.choices[0].message.content
-        
-        contents.append(Content.from_text(response_content))
-        
-        return contents