Delete app.py

app.py

@@ -1,99 +0,0 @@
-import os
-from dotenv import load_dotenv
-from fastapi import FastAPI, File, UploadFile, HTTPException, Header
-from pydantic import BaseModel
-from typing import List, Optional
-import torch
-from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
-from qwen_vl_utils import process_vision_info
-from byaldi import RAGMultiModalModel
-from PIL import Image
-import io
-
-# Load environment variables
-load_dotenv()
-
-# Access environment variables
-HF_TOKEN = os.getenv("HF_TOKEN")
-RAG_MODEL = os.getenv("RAG_MODEL", "vidore/colpali")
-QWN_MODEL = os.getenv("QWN_MODEL", "Qwen/Qwen2-VL-7B-Instruct")
-QWN_PROCESSOR = os.getenv("QWN_PROCESSOR", "Qwen/Qwen2-VL-2B-Instruct")
-
-if not HF_TOKEN:
-    raise ValueError("HF_TOKEN not found in .env file")
-
-# Initialize FastAPI app
-app = FastAPI()
-
-# Load models and processors
-RAG = RAGMultiModalModel.from_pretrained(RAG_MODEL, use_auth_token=HF_TOKEN)
-
-model = Qwen2VLForConditionalGeneration.from_pretrained(
-    QWN_MODEL,
-    torch_dtype=torch.bfloat16,
-    attn_implementation="flash_attention_2",
-    device_map="auto",
-    trust_remote_code=True,
-    use_auth_token=HF_TOKEN
-).cuda().eval()
-
-processor = AutoProcessor.from_pretrained(QWN_PROCESSOR, trust_remote_code=True, use_auth_token=HF_TOKEN)
-
-# Define request model
-class DocumentRequest(BaseModel):
-    text_query: str
-
-# Define processing function
-def document_rag(text_query, image):
-    messages = [
-        {
-            "role": "user",
-            "content": [
-                {
-                    "type": "image",
-                    "image": image,
-                },
-                {"type": "text", "text": text_query},
-            ],
-        }
-    ]
-    text = processor.apply_chat_template(
-        messages, tokenize=False, add_generation_prompt=True
-    )
-    image_inputs, video_inputs = process_vision_info(messages)
-    inputs = processor(
-        text=[text],
-        images=image_inputs,
-        videos=video_inputs,
-        padding=True,
-        return_tensors="pt",
-    )
-    inputs = inputs.to("cuda")
-    generated_ids = model.generate(**inputs, max_new_tokens=50)
-    generated_ids_trimmed = [
-        out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
-    ]
-    output_text = processor.batch_decode(
-        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
-    )
-    return output_text[0]
-
-# Define API endpoints
-@app.post("/process_document")
-async def process_document(request: DocumentRequest, file: UploadFile = File(...), x_api_key: Optional[str] = Header(None)):
-    # Check API key
-    if x_api_key != HF_TOKEN:
-        raise HTTPException(status_code=403, detail="Invalid API key")
-    
-    # Read and process the uploaded file
-    contents = await file.read()
-    image = Image.open(io.BytesIO(contents))
-    
-    # Process the document
-    result = document_rag(request.text_query, image)
-    
-    return {"result": result}
-
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8000)