from fastapi import FastAPI, File, UploadFile from fastapi.responses import JSONResponse from fastapi.middleware.cors import CORSMiddleware from PIL import Image import io import json from utils import get_text, translate_text, chat_text from json_flatten import flatten from pydantic import BaseModel from typing import Optional app = FastAPI( title="DOCUMANTICAI API", description=""" This API allows you to upload an image and get a formatted response with details and image information. """ ) app.add_middleware( CORSMiddleware, allow_origins="*" ) @app.post("/upload") async def upload_image(fields:str, model:str, file: UploadFile = File(...)): """ ### Endpoint Description: Extract form data from an uploaded image and return the extracted data in JSON format. #### Request Parameters: - `file`: The image file to extract data from. (Required) #### Response: ### Notes: - The image should be in a supported format (e.g., PNG, JPEG). - The data extracted will vary depending on the image content. """ try: # Load the uploaded image image = Image.open(io.BytesIO(await file.read())) # Example: Get image details image_details = { "filename": file.filename, "format": image.format, "size": image.size, # (width, height) "mode": image.mode } response = get_text(image,image_details['filename'], model, fields) # Step 1: Convert the escaped JSON string to a proper dictionary # Step 2: Convert the response to a proper dictionary response = json.loads(response) # Step 3: Convert fields and values into key-value pairs if 'fields' in response and 'values' in response: response = dict(zip(response['fields'], response['values'])) # response flattening response = flatten(response) # Process image (example: return metadata) return JSONResponse(content={"response": response, "details": image_details}) except Exception as e: return JSONResponse(content={"error": str(e)}, status_code=400) @app.get("/models") async def list_models(): """ ### Endpoint Description: List available models for text generation. #### Response: - A list of available models for text generation. """ models = [ { "id": "gpt-4o-mini", "description": "Latest model by OpenAI prefered for accuracy", "title": "GPT-4.o Mini", }, { "id": "gpt-4o", "description": "Latest model by OpenAI prefered for accuracy", "title": "GPT-4.o", }, { "id": "deepseek-chat", "description": "Latest model by Deepseek prefered for speed", "title": "DeepSeek Chat", }, { "id": "claude-3-5-sonnet-20241022", "description": "Latest model by Google for both accuracy and speed", "title": "Claude 3.5 Sonnet 20241022", }, { "id": "llama_llm_d", "description": "Latest model by Facebook for both accuracy and speed", "title": "Llama LLM D", }, { "id": "llama_llm_o", "description": "Latest model by Facebook for both accuracy and speed", "title": "Llama LLM O", } ] return JSONResponse(content={"models": models}) @app.get("/translate") async def translate_text_endpoint(text: str, target_language: str): """ ### Endpoint Description: Translate text to a specified language. #### Request Parameters: - `text`: The text to translate. (Required) - `target_language`: The language to translate the text to. (Required) #### Response: - The translated text in the specified language. """ try: # print(target_language) response = translate_text(text, target_language) # print(response) #{"translated_text":"नमस्ते","translated_language":"नेपाली"} response = json.loads(response) return JSONResponse(content=response) except Exception as e: return JSONResponse(content={"error": str(e)}, status_code=400) class ChatRequest(BaseModel): query: str text: str history: Optional[str] = None @app.post("/chat") def chat_endpoint(request: ChatRequest): response_text = chat_text(request.text, request.query, request.history) return {"reply": response_text}