Update app.py

app.py

@@ -1,94 +1,90 @@
-import spaces
-import gradio as gr
-from pdf2image import convert_from_path
-from byaldi import RAGMultiModalModel
-from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
-from qwen_vl_utils import process_vision_info
-import torch
-import subprocess
-
-# Install flash-attn if not already installed
-subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
-
-# try:
-#     subprocess.check_output(['dpkg', '-s', 'poppler-utils'])
-# except subprocess.CalledProcessError:
-#     print("Error: poppler-utils is not installed. Installing...")
-#     subprocess.check_call(['sudo', 'apt-get', 'install', '-y', 'poppler-utils'])
-
-# Load the RAG Model and the Qwen2-VL-2B-Instruct model
-RAG = RAGMultiModalModel.from_pretrained("vidore/colpali")
-model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-2B-Instruct",
-                                                        trust_remote_code=True, torch_dtype=torch.bfloat16).cuda().eval()
-processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", trust_remote_code=True)
-
-@spaces.GPU()
-def process_pdf_and_query(pdf_file, user_query):
-    # Convert the PDF to images
-    images = convert_from_path(pdf_file.name)  # pdf_file.name gives the file path
-    num_images = len(images)
-
-    # Indexing the PDF in RAG
-    RAG.index(
-        input_path=pdf_file.name,
-        index_name="image_index",  # index will be saved at index_root/index_name/
-        store_collection_with_index=False,
-        overwrite=True
-    )
-
-    # Search the query in the RAG model
-    results = RAG.search(user_query, k=1)
-    if not results:
-        return "No results found.", num_images
-
-    # Retrieve the page number and process image
-    image_index = results[0]["page_num"] - 1
-    messages = [
-        {
-            "role": "user",
-            "content": [
-                {
-                    "type": "image",
-                    "image": images[image_index],
-                },
-                {"type": "text", "text": user_query},
-            ],
-        }
-    ]
-
-    # Generate text with the Qwen model
-    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")
-    
-    # Generate the output response
-    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], num_images
-
-# Define the Gradio Interface
-pdf_input = gr.inputs.File(label="Upload PDF", type="file")
-query_input = gr.inputs.Textbox(label="Enter your query", placeholder="Ask a question about the PDF")
-output_text = gr.outputs.Textbox(label="Model Answer")
-output_images = gr.outputs.Textbox(label="Number of Images in PDF")
-
-# Launch the Gradio app
-gr.Interface(
-    fn=process_pdf_and_query, 
-    inputs=[pdf_input, query_input], 
-    outputs=[output_text, output_images],
-    title="Multimodal RAG with Image Query - By Pejman Ebrahimi"
-).launch()
+import spaces
+import gradio as gr
+from pdf2image import convert_from_path
+from byaldi import RAGMultiModalModel
+from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
+from qwen_vl_utils import process_vision_info
+import torch
+import subprocess
+
+# Install flash-attn if not already installed
+subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
+
+# Load the RAG Model and the Qwen2-VL-2B-Instruct model
+RAG = RAGMultiModalModel.from_pretrained("vidore/colpali")
+model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-2B-Instruct",
+                                                        trust_remote_code=True, torch_dtype=torch.bfloat16).cuda().eval()
+processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", trust_remote_code=True)
+
+@spaces.GPU()
+def process_pdf_and_query(pdf_file, user_query):
+    # Convert the PDF to images
+    images = convert_from_path(pdf_file.name)  # pdf_file.name gives the file path
+    num_images = len(images)
+
+    # Indexing the PDF in RAG
+    RAG.index(
+        input_path=pdf_file.name,
+        index_name="image_index",  # index will be saved at index_root/index_name/
+        store_collection_with_index=False,
+        overwrite=True
+    )
+
+    # Search the query in the RAG model
+    results = RAG.search(user_query, k=1)
+    if not results:
+        return "No results found.", num_images
+
+    # Retrieve the page number and process image
+    image_index = results[0]["page_num"] - 1
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "image",
+                    "image": images[image_index],
+                },
+                {"type": "text", "text": user_query},
+            ],
+        }
+    ]
+
+    # Generate text with the Qwen model
+    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")
+    
+    # Generate the output response
+    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], num_images
+
+# Define the Gradio Interface
+pdf_input = gr.File(label="Upload PDF")  # Single PDF file input
+query_input = gr.Textbox(label="Enter your query", placeholder="Ask a question about the PDF")  # User query input
+output_text = gr.Textbox(label="Model Answer")  # Output for the model's answer
+output_images = gr.Textbox(label="Number of Images in PDF")  # Output for number of images
+
+# Launch the Gradio app
+demo = gr.Interface(
+    fn=process_pdf_and_query, 
+    inputs=[pdf_input, query_input],  # List of inputs
+    outputs=[output_text, output_images],  # List of outputs
+    title="Multimodal RAG with Image Query - By Pejman Ebrahimi"
+)
+
+demo.launch(debug=True)  # Start the interface