Add application file

app.py

@@ -0,0 +1,76 @@
+import streamlit as st
+import torch
+from transformers import AutoModelForCausalLM, AutoProcessor, AutoTokenizer
+from PIL import Image
+from byaldi import RAGMultiModalModel
+from qwen_vl_utils import process_vision_info
+
+# Model and processor names
+RAG_MODEL = "vidore/colpali"
+QWN_MODEL = "Qwen/Qwen2-VL-7B-Instruct"
+QWN_PROCESSOR = "Qwen/Qwen2-VL-2B-Instruct"
+
+@st.cache_resource
+def load_models():
+    RAG = RAGMultiModalModel.from_pretrained(RAG_MODEL)
+    
+    model = AutoModelForCausalLM.from_pretrained(
+        QWN_MODEL,
+        torch_dtype=torch.bfloat16,
+        trust_remote_code=True
+    ).cuda().eval()
+    
+    processor = AutoProcessor.from_pretrained(QWN_PROCESSOR, trust_remote_code=True)
+    tokenizer = AutoTokenizer.from_pretrained(QWN_PROCESSOR, trust_remote_code=True)
+    
+    return RAG, model, processor, tokenizer
+
+RAG, model, processor, tokenizer = load_models()
+
+def document_rag(text_query, image):
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "image",
+                    "image": image,
+                },
+                {"type": "text", "text": text_query},
+            ],
+        }
+    ]
+    text = tokenizer.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 = tokenizer.batch_decode(
+        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+    )
+    return output_text[0]
+
+st.title("Document Processor")
+
+uploaded_file = st.file_uploader("Choose an image file", type=["jpg", "jpeg", "png"])
+text_query = st.text_input("Enter your text query")
+
+if uploaded_file is not None and text_query:
+    image = Image.open(uploaded_file)
+    
+    if st.button("Process Document"):
+        with st.spinner("Processing..."):
+            result = document_rag(text_query, image)
+        st.success("Processing complete!")
+        st.write("Result:", result)