first

README.md

@@ -1,13 +1,10 @@
 ---
-title: Nv Reason Cxr
-emoji: 📉
-colorFrom: blue
-colorTo: yellow
+title: NV-Reason-CXR-3B Demo
+emoji: 🩻
+colorFrom: green
+colorTo: gray
 sdk: gradio
-sdk_version: 5.49.1
+sdk_version: 5.44.1
 app_file: app.py
-pinned: false
-short_description: NV-Reason-CXR-3B Demo
+license: apache-2.0
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,156 @@
+import gradio as gr
+from transformers import AutoProcessor, AutoModelForImageTextToText, TextIteratorStreamer
+from threading import Thread
+import torch
+import spaces
+import os
+
+pretrained_model_name_or_path=os.environ.get("MODEL", "nvidia/NV-Reason-CXR-3B")
+
+auth_token = os.environ.get("HF_TOKEN") or True
+DEFAULT_PROMPT = "Find abnormalities and support devices."
+
+model = AutoModelForImageTextToText.from_pretrained(
+    pretrained_model_name_or_path=pretrained_model_name_or_path,
+    dtype=torch.bfloat16,
+    token=auth_token
+).eval().to("cuda")
+
+
+processor = AutoProcessor.from_pretrained(pretrained_model_name_or_path,
+    use_fast=True,
+  )
+
+
+@spaces.GPU
+def model_inference(
+    text, history, image
+): 
+
+    print(f"text: {text}")
+    print(f"history: {history}")
+
+    if len(text) == 0:
+        raise gr.Error("Please input a query.", duration=3, print_exception=False)
+
+    if image is None:
+        raise gr.Error("Please provide an image.", duration=3, print_exception=False)
+
+    # print(f"image0: {image} size: {image.size}")
+
+    messages=[]
+    if len(history) > 0:
+        valid_index = None
+        for i in range(len(history)):
+            h = history[i]
+            if len(h.get("content").strip()) > 0:
+                if valid_index is None and h['role'] == 'assistant':
+                    valid_index = i-1 
+                messages.append({"role": h['role'], "content": [{"type": "text", "text": h['content']}] })
+
+        if valid_index is None:
+            messages = []
+        if len(messages) > 0 and valid_index > 0:
+            messages = messages[valid_index:] #remove previous messages (without image)
+
+    # current prompt
+    messages.append({"role": "user","content": [{"type": "text", "text": text}]})
+    messages[0]['content'].insert(0, {"type": "image"})
+    print(f"messages: {messages}")
+
+
+    prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
+    inputs = processor(text=prompt, images=[image], return_tensors="pt")
+    inputs = inputs.to('cuda')
+
+
+    # Generate
+    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
+    generation_args = dict(inputs, streamer=streamer, max_new_tokens=4096)
+
+    with torch.inference_mode():
+        thread = Thread(target=model.generate, kwargs=generation_args)
+        thread.start()
+
+        yield "..."
+        buffer = ""
+        
+        
+        for new_text in streamer:
+            buffer += new_text
+            yield buffer
+
+
+with gr.Blocks() as demo:
+
+    # gr.Markdown('<h1 style="text-align:center; margin: 0.2em 0;">Demo.</h1>')
+    send_btn = gr.Button("Send", variant="primary", render=False)
+    textbox = gr.Textbox(show_label=False, placeholder="Enter your text here and press ENTER", render=False, submit_btn="Send")
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            image_input = gr.Image(type="pil", visible=True, sources="upload", show_label=False)
+
+            clear_btn = gr.Button("Clear", variant="secondary")
+
+            ex =gr.Examples(
+                examples=[
+                    ["example_images/35.jpg", "Examine the chest X-ray."],
+                    ["example_images/363.jpg", "Provide a comprehensive image analysis, and list all abnormalities."],
+                    ["example_images/4747.jpg", "Find abnormalities and support devices."],
+                    ["example_images/87.jpg", "Find abnormalities and support devices."],
+                    ["example_images/6218.jpg", "Find abnormalities and support devices."],
+                    ["example_images/6447.jpg", "Find abnormalities and support devices."],
+
+
+                ],
+                inputs=[image_input, textbox],
+            )
+
+        with gr.Column(scale=2):
+            chat_interface = gr.ChatInterface(fn=model_inference,
+                type="messages",
+                chatbot=gr.Chatbot(type="messages", label="AI", render_markdown=True, sanitize_html=False, allow_tags=True, height='35vw', container=False, show_share_button=False),
+                textbox=textbox,
+                additional_inputs=image_input,
+                multimodal=False,
+                fill_height=False,
+                show_api=False,
+                )
+            gr.HTML('<span style="color:lightgray">Start with a full prompt: Find abnormalities and support devices.<br>\
+                Follow up with additial questions, such as Provide differentials or Write a structured report.<br>')
+
+
+
+        # Clear chat history when an example is selected (keep example-populated inputs intact)
+        ex.load_input_event.then(
+                lambda: ([], [], [], None),
+                None,
+                [chat_interface.chatbot, chat_interface.chatbot_state, chat_interface.chatbot_value, chat_interface.saved_input],
+                queue=False,
+                show_api=False,
+            )
+               
+        # Clear chat history when a new image is uploaded via the image input
+        image_input.upload(
+                lambda: ([], [], [], None, DEFAULT_PROMPT),
+                None,
+                [chat_interface.chatbot, chat_interface.chatbot_state, chat_interface.chatbot_value, chat_interface.saved_input, textbox],
+                queue=False,
+                show_api=False,
+            )
+
+        # Clear everything on Clear button click
+        clear_btn.click(
+                lambda: ([], [], [], None, "", None),
+                None,
+                [chat_interface.chatbot, chat_interface.chatbot_state, chat_interface.chatbot_value, chat_interface.saved_input, textbox, image_input],
+                queue=False,
+                show_api=False,
+            )
+
+
+
+demo.queue(max_size=10)
+demo.launch(debug=False, server_name="0.0.0.0")
+        

requirements.txt

@@ -0,0 +1,8 @@
+torch==2.7.1
+torchvision==0.22.1
+transformers==4.56.0
+huggingface_hub
+gradio==5.44.1
+spaces==0.40.1
+# qwen_vl_utils==0.0.11
+