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app.py

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+import gradio as gr
+import spaces
+from transformers import Qwen2VLForConditionalGeneration, AutoProcessor, TextIteratorStreamer
+from qwen_vl_utils import process_vision_info
+import torch
+from PIL import Image
+import subprocess
+import numpy as np
+import os
+from threading import Thread
+import uuid
+import io
+
+# Model and Processor Loading (Done once at startup)
+MODEL_ID = "Qwen/Qwen2-VL-2B-Instruct"
+model = Qwen2VLForConditionalGeneration.from_pretrained(
+    MODEL_ID,
+    trust_remote_code=True,
+    torch_dtype=torch.float16
+).to("cuda").eval()
+processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
+
+DESCRIPTION = "[Qwen2-VL-2B Demo](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct)"
+
+image_extensions = Image.registered_extensions()
+video_extensions = ("avi", "mp4", "mov", "mkv", "flv", "wmv", "mjpeg", "wav", "gif", "webm", "m4v", "3gp")
+
+
+def identify_and_save_blob(blob_path):
+    """Identifies if the blob is an image or video and saves it accordingly."""
+    try:
+        with open(blob_path, 'rb') as file:
+            blob_content = file.read()
+            
+            # Try to identify if it's an image
+            try:
+                Image.open(io.BytesIO(blob_content)).verify()  # Check if it's a valid image
+                extension = ".png"  # Default to PNG for saving
+                media_type = "image"
+            except (IOError, SyntaxError):
+                # If it's not a valid image, assume it's a video
+                extension = ".mp4"  # Default to MP4 for saving
+                media_type = "video"
+            
+            # Create a unique filename
+            filename = f"temp_{uuid.uuid4()}_media{extension}"
+            with open(filename, "wb") as f:
+                f.write(blob_content)
+                
+            return filename, media_type
+            
+    except FileNotFoundError:
+        raise ValueError(f"The file {blob_path} was not found.")
+    except Exception as e:
+        raise ValueError(f"An error occurred while processing the file: {e}")
+
+
+@spaces.GPU
+def qwen_inference(media_input, text_input=None):
+    if isinstance(media_input, str):  # If it's a filepath
+        media_path = media_input
+        if media_path.endswith(tuple([i for i, f in image_extensions.items()])):
+            media_type = "image"
+        elif media_path.endswith(video_extensions): 
+            media_type = "video"
+        else:
+            try:
+                media_path, media_type = identify_and_save_blob(media_input)
+                print(media_path, media_type)
+            except Exception as e:
+                print(e)
+                raise ValueError(
+                    "Unsupported media type. Please upload an image or video."
+                )
+        
+
+    print(media_path)
+
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": media_type,
+                    media_type: media_path,
+                    **({"fps": 8.0} if media_type == "video" else {}),
+                },
+                {"type": "text", "text": text_input},
+            ],
+        }
+    ]
+
+    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",
+    ).to("cuda")
+
+    streamer = TextIteratorStreamer(
+        processor, skip_prompt=True, **{"skip_special_tokens": True}
+    )
+    generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024)
+
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+
+    buffer = ""
+    for new_text in streamer:
+        buffer += new_text
+        yield buffer
+
+css = """
+  #output {
+    height: 500px; 
+    overflow: auto; 
+    border: 1px solid #ccc; 
+  }
+"""
+
+with gr.Blocks(css=css) as demo:
+    gr.Markdown(DESCRIPTION)
+
+    with gr.Tab(label="Image/Video Input"):
+        with gr.Row():
+            with gr.Column():
+                input_media = gr.File(
+                    label="Upload Image or Video", type="filepath" 
+                )
+                text_input = gr.Textbox(label="Question")
+                submit_btn = gr.Button(value="Submit")
+            with gr.Column():
+                output_text = gr.Textbox(label="Output Text")
+
+        submit_btn.click(
+            qwen_inference, [input_media, text_input], [output_text]
+        )
+
+demo.launch(debug=True)

requirements.txt

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+numpy==1.24.4
+Pillow==10.3.0
+Requests==2.31.0
+torch
+torchvision
+git+https://github.com/huggingface/transformers.git
+accelerate
+qwen-vl-utils
+av