remove ai

ocr/api/message/openai_request.py

@@ -1,17 +0,0 @@
-from ocr.api.message.prompts import OCRPrompts
-from ocr.core.wrappers import openai_wrapper
-
-
-@openai_wrapper(model='gpt-4o-mini')
-async def generate_report(request_content: list[dict]):
-    messages = [
-        {
-            "role": "system",
-            "content": OCRPrompts.generate_general_answer
-        },
-        {
-            "role": "user",
-            "content": request_content
-        }
-    ]
-    return messages

ocr/api/message/prompts.py

@@ -1,44 +0,0 @@
-class OCRPrompts:
-    generate_general_answer = """## Task
-
-You must analyze the attached medical document and generate a comprehensive report in **Markdown2** format. Ensure that every detail provided in the document is included, and do not omit or modify any information. Your output must strictly follow the required format.
-
-## Report Structure
-
-The report should be structured as follows, with each section containing only relevant information from the document:
-
-```markdown
-## Patient Information
-
-- Name: [Patient Name]
-- Age: [Patient Age]
-- Date of Scan: [Date]
-- Indication: [Reason for the CT scan]
-
-## Findings
-
-**Primary findings**:
-[Describe significant abnormalities or findings relevant to the indication]
-
-** Secondary findings**:
-[List incidental findings, e.g., "Mild hepatic steatosis noted."]
-**No abnormalities**:
-[Mention organs or systems without abnormalities, e.g., "No evidence of lymphadenopathy or pleural effusion."]
-
-## Impression
-
-[Summarize the findings concisely, e.g., "Findings suggest a primary lung tumor. Biopsy recommended for further evaluation."]
-
-## Recommendations
-
-[Include next steps or further tests, e.g., "PET scan and consultation with oncology recommended."]
-```
-
-[INST]
-
-## Instructions
-
-- **Do not invent or infer any information.** Only use data provided in the document.
-- Ensure that the format is followed strictly, and the output is complete without any deviations.
-
-[/INST]"""

ocr/api/message/views.py

@@ -1,7 +1,6 @@
 from fastapi import File, UploadFile
 
 from ocr.api.message import ocr_router
-from ocr.api.message.openai_request import generate_report
 from ocr.api.message.schemas import OcrResponse
 from ocr.api.message.utils import divide_images, prepare_request_content, clean_response
 from ocr.core.wrappers import OcrResponseWrapper
@@ -13,9 +12,6 @@ async def get_all_chat_messages(
 ) -> OcrResponseWrapper[OcrResponse]:
     try:
         contents = await file.read()
-        images = divide_images(contents)
-        request_content = prepare_request_content(images)
-        response = await generate_report(request_content)
-        return OcrResponseWrapper(data=OcrResponse(text=clean_response(response)))
+        return OcrResponseWrapper(data=OcrResponse(text=clean_response("## Coming soon")))
     finally:
         await file.close()

ocr/core/wrappers.py

@@ -1,14 +1,10 @@
-import json
 from functools import wraps
 from typing import Generic, Optional, TypeVar
 
-import pydash
 from fastapi import HTTPException
 from pydantic import BaseModel
 from starlette.responses import JSONResponse
 
-from ocr.core.config import settings
-
 T = TypeVar('T')
 
 
@@ -46,32 +42,6 @@ def exception_wrapper(http_error: int, error_message: str):
     return decorator
 
 
-def openai_wrapper(
-        temperature: int | float = 0, model: str = "gpt-4o-mini", is_json: bool = False, return_: str = None
-):
-    def decorator(func):
-        @wraps(func)
-        async def wrapper(*args, **kwargs) -> str:
-            messages = await func(*args, **kwargs)
-            completion = await settings.OPENAI_CLIENT.chat.completions.create(
-                messages=messages,
-                temperature=temperature,
-                n=1,
-                model=model,
-                response_format={"type": "json_object"} if is_json else {"type": "text"}
-            )
-            response = completion.choices[0].message.content
-            if is_json:
-                response = json.loads(response)
-                if return_:
-                    return pydash.get(response, return_)
-            return response
-
-        return wrapper
-
-    return decorator
-
-
 def background_task():
     def decorator(func):
         @wraps(func)