Update app.py

app.py

@@ -1,76 +1,176 @@
 import streamlit as st
 import torch
 from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
-from PIL import Image
-from byaldi import RAGMultiModalModel
 from qwen_vl_utils import process_vision_info
+from byaldi import RAGMultiModalModel
+from PIL import Image
+import io
+import time
+import nltk
+from nltk.translate.bleu_score import sentence_bleu
 
-# Model and processor names
-RAG_MODEL = "vidore/colpali"
-QWN_MODEL = "Qwen/Qwen2-VL-7B-Instruct"
+# Download NLTK data for BLEU score calculation
+nltk.download('punkt', quiet=True)
 
+# Load models and processors
 @st.cache_resource
 def load_models():
-    RAG = RAGMultiModalModel.from_pretrained(RAG_MODEL)
+    RAG = RAGMultiModalModel.from_pretrained("vidore/colpali")
     
-    model = Qwen2VLForConditionalGeneration.from_pretrained(
-        QWN_MODEL,
+    qwen_model = Qwen2VLForConditionalGeneration.from_pretrained(
+        "Qwen/Qwen2-VL-7B-Instruct",
         torch_dtype=torch.bfloat16,
         attn_implementation="flash_attention_2",
         device_map="auto",
         trust_remote_code=True
-    ).eval()
+    ).cuda().eval()
     
-    processor = AutoProcessor.from_pretrained(QWN_MODEL, trust_remote_code=True)
+    qwen_processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", trust_remote_code=True)
     
-    return RAG, model, processor
+    return RAG, qwen_model, qwen_processor
+
+RAG, qwen_model, qwen_processor = load_models()
+
+# Function to get current CUDA memory usage
+def get_cuda_memory_usage():
+    return torch.cuda.memory_allocated() / 1024**2  # Convert to MB
 
-RAG, model, processor = load_models()
+# Define processing functions
+def extract_text_with_colpali(image):
+    start_time = time.time()
+    start_memory = get_cuda_memory_usage()
+    
+    extracted_text = RAG.extract_text(image)
+    
+    end_time = time.time()
+    end_memory = get_cuda_memory_usage()
+    
+    return extracted_text, {
+        'time': end_time - start_time,
+        'memory': end_memory - start_memory
+    }
 
-def document_rag(text_query, image):
+def process_with_qwen(query, extracted_text, image, extract_mode=False):
+    start_time = time.time()
+    start_memory = get_cuda_memory_usage()
+    
+    if extract_mode:
+        instruction = "Extract and list all text visible in this image, including both printed and handwritten text."
+    else:
+        instruction = f"Context: {extracted_text}\n\nQuery: {query}"
+    
     messages = [
         {
             "role": "user",
             "content": [
+                {
+                    "type": "text",
+                    "text": instruction
+                },
                 {
                     "type": "image",
                     "image": image,
                 },
-                {"type": "text", "text": text_query},
             ],
         }
     ]
-    text = processor.apply_chat_template(
+    text = qwen_processor.apply_chat_template(
         messages, tokenize=False, add_generation_prompt=True
     )
     image_inputs, video_inputs = process_vision_info(messages)
-    inputs = processor(
+    inputs = qwen_processor(
         text=[text],
         images=image_inputs,
         videos=video_inputs,
         padding=True,
         return_tensors="pt",
     )
-    inputs = inputs.to(model.device)
-    generated_ids = model.generate(**inputs, max_new_tokens=50)
+    inputs = inputs.to("cuda")
+    generated_ids = qwen_model.generate(**inputs, max_new_tokens=200)
     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(
+    output_text = qwen_processor.batch_decode(
         generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
     )
-    return output_text[0]
+    
+    end_time = time.time()
+    end_memory = get_cuda_memory_usage()
+    
+    return output_text[0], {
+        'time': end_time - start_time,
+        'memory': end_memory - start_memory
+    }
 
-st.title("Document Processor")
+# Function to calculate BLEU score
+def calculate_bleu(reference, hypothesis):
+    reference_tokens = nltk.word_tokenize(reference.lower())
+    hypothesis_tokens = nltk.word_tokenize(hypothesis.lower())
+    return sentence_bleu([reference_tokens], hypothesis_tokens)
 
-uploaded_file = st.file_uploader("Choose an image file", type=["jpg", "jpeg", "png"])
-text_query = st.text_input("Enter your text query")
+# Streamlit UI
+st.title("Document Processing with ColPali and Qwen")
 
-if uploaded_file is not None and text_query:
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+query = st.text_input("Enter your query:")
+
+if uploaded_file is not None and query:
     image = Image.open(uploaded_file)
-    
-    if st.button("Process Document"):
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    if st.button("Process"):
         with st.spinner("Processing..."):
-            result = document_rag(text_query, image)
-        st.success("Processing complete!")
-        st.write("Result:", result)
+            # Extract text using ColPali
+            colpali_extracted_text, colpali_metrics = extract_text_with_colpali(image)
+            
+            # Extract text using Qwen
+            qwen_extracted_text, qwen_extract_metrics = process_with_qwen("", "", image, extract_mode=True)
+            
+            # Process the query with Qwen2, using both extracted text and image
+            qwen_response, qwen_response_metrics = process_with_qwen(query, colpali_extracted_text, image)
+            
+            # Calculate BLEU score between ColPali and Qwen extractions
+            bleu_score = calculate_bleu(colpali_extracted_text, qwen_extracted_text)
+
+        # Display results
+        st.subheader("Results")
+        st.write("ColPali Extracted Text:")
+        st.write(colpali_extracted_text)
+        
+        st.write("Qwen Extracted Text:")
+        st.write(qwen_extracted_text)
+        
+        st.write("Qwen Response:")
+        st.write(qwen_response)
+
+        # Display metrics
+        st.subheader("Metrics")
+        
+        st.write("ColPali Extraction:")
+        st.write(f"Time: {colpali_metrics['time']:.2f} seconds")
+        st.write(f"Memory: {colpali_metrics['memory']:.2f} MB")
+        
+        st.write("Qwen Extraction:")
+        st.write(f"Time: {qwen_extract_metrics['time']:.2f} seconds")
+        st.write(f"Memory: {qwen_extract_metrics['memory']:.2f} MB")
+        
+        st.write("Qwen Response:")
+        st.write(f"Time: {qwen_response_metrics['time']:.2f} seconds")
+        st.write(f"Memory: {qwen_response_metrics['memory']:.2f} MB")
+        
+        st.write(f"BLEU Score: {bleu_score:.4f}")
+
+st.markdown("""
+## How to Use
+
+1. Upload an image containing text or a document.
+2. Enter your query about the document.
+3. Click 'Process' to see the results.
+
+The app will display:
+- Text extracted by ColPali
+- Text extracted by Qwen
+- Qwen's response to your query
+- Performance metrics for each step
+- BLEU score comparing ColPali and Qwen extractions
+""")