Create app.py

app.py

@@ -0,0 +1,86 @@
+import os
+from fastapi import FastAPI, File, UploadFile
+from pydantic import BaseModel
+from typing import List
+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
+
+# Initialize FastAPI app
+app = FastAPI()
+
+# Define model and processor paths
+RAG_MODEL = "vidore/colpali"
+QWN_MODEL = "Qwen/Qwen2-VL-7B-Instruct"
+QWN_PROCESSOR = "Qwen/Qwen2-VL-2B-Instruct"
+
+# Load models and processors
+RAG = RAGMultiModalModel.from_pretrained(RAG_MODEL)
+
+model = Qwen2VLForConditionalGeneration.from_pretrained(
+    QWN_MODEL,
+    torch_dtype=torch.bfloat16,
+    attn_implementation="flash_attention_2",
+    device_map="auto",
+    trust_remote_code=True
+).cuda().eval()
+
+processor = AutoProcessor.from_pretrained(QWN_PROCESSOR, trust_remote_code=True)
+
+# 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(...)):
+    # 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)