setup is added to handle linux command

app.py

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+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()

requirements.txt

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+spaces
+gradio
+byaldi
+poppler-utils
+pdf2image 
+git+https://github.com/huggingface/transformers.git 
+qwen-vl-utils 
+torchvision
+torch

setup.sh

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+#!/bin/bash
+
+# Install poppler-utils
+sudo apt-get update
+sudo apt-get install -y poppler-utils