Update app.py

reason1 demo draft

app.py

@@ -1,154 +1,286 @@
 import gradio as gr
-import numpy as np
-import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
 import torch
+import os
+import json
+from transformers import AutoProcessor, AutoModelForCausalLM, AutoModelForVision2Seq
+from qwen_vl_utils import process_vision_info
+
+# Model configuration
+MODEL_PATH = "nvidia/Cosmos-Reason1-7B"
+
+# Role configurations
+ROLES = {
+    "General Assistant": "You are a helpful assistant. Answer the question in the following format: \n<think>\nyour reasoning\n</think>\n\n<answer>\nyour answer\n</answer>.",
+    "Video Analyzer": """You are a helpful video analyzer. The goal is to identify artifacts and anomalies in the video. Watch carefully and focus on the following details:
+
+* Physical accuracy (gravity, collision, object interaction, fluid dynamics, object permanence, etc.)
+* Common sense
+* Cause-and-effect
+* Temporal consistency
+* Spatial consistency
+* Human motion
+* Material and Texture realism
+
+Here are some examples of commonly found artifacts and anomalies:
+
+* If objects penetrate each other, this indicates a failure in collision detection, object interaction, and physical accuracy.
+* If hands penetrate each other, or hands pass through objects, this indicates a failure in collision detection, object interaction, and physical accuracy.
+* If an object moves in an unexpected way or move without any apparent reason, this suggests a failure in causality, object interaction, and physical accuracy.
+* If an object suddenly flips or changes direction, this suggests a failure in temporal consistency.
+* If an object suddenly appears or disappears, or the count of objects in the video suddenly changes, this suggests a failure in temporal consistency.
+* If an object transforms or deforms half way through the video, this suggests a failure in temporal consistency.
+* If an object is used in a way that defies its intended purpose or normal function, this indicates a violation of common sense.
+* If the liquid flows through a solid object, such as water flowing through a pan, this suggests a failure in physical accuracy and fluid dynamics.
+* If a person's legs or arms suddenly switch positions in an impossible way—such as the left leg appearing where the right leg was just a moment ago, this suggests a failure in human motion and temporal consistency.
+* If a person's body suddenly morphs or changes shape, this suggests a failure in human motion and temporal consistency.
+* If an object's texture, material or surface is unnaturally smooth, this suggests a failure in object surface reconstruction.
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
+Here are some examples of non-artifacts you should not include in your analysis:
+
+* Being an animated video, such as a cartoon, does not automatically make it artifacts.
+* Avoid ungrounded and over-general explanations such as overall impression, artistic style, or background elements.
+* The video has no sound. Avoid explanations based on sound.
+* Do not mention lighting, shadows, blurring, or camera effects in your analysis.
+
+Answer the question in English with provided options in the following format:
+<think>\nyour reasoning\n</think>\n\n<answer>\nyour answer\n</answer>.""",
+    "Custom Role": "You are a helpful assistant. Answer the question in the following format: \n<think>\nyour reasoning\n</think>\n\n<answer>\nyour answer\n</answer>."
 }
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
 
-            run_button = gr.Button("Run", scale=0, variant="primary")
+# Default configuration
+default_config = {
+    "attention_mode": "sdpa",
+    "torch_dtype": "float16",
+    "device_map": "auto",
+    "trust_remote_code": True
+}
 
-        result = gr.Image(label="Result", show_label=False)
+# Load or create config file
+config_file = "cosmos_reason1_config.json"
+try:
+    if not os.path.exists(config_file):
+        with open(config_file, "w") as f:
+            json.dump(default_config, f, indent=4)
+        config = default_config
+    else:
+        with open(config_file, "r") as f:
+            config = json.load(f)
+except Exception as e:
+    print(f"Warning: Could not load config file: {e}")
+    print("Using default configuration")
+    config = default_config
 
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
+# Initialize the model with configuration
+try:
+    model = AutoModelForVision2Seq.from_pretrained(
+        MODEL_PATH,
+        torch_dtype=getattr(torch, config["torch_dtype"]),
+        device_map=config["device_map"],
+        trust_remote_code=config["trust_remote_code"]
+    )
+except Exception as e:
+    print(f"Error loading model: {e}")
+    raise
 
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
+# Initialize sampling parameters
+generation_config = {
+    "temperature": 0.6,
+    "top_p": 0.95,
+    "repetition_penalty": 1.05,
+    "max_new_tokens": 4096,
+}
 
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+# Initialize the processor
+try:
+    processor = AutoProcessor.from_pretrained(MODEL_PATH)
+except Exception as e:
+    print(f"Error loading processor: {e}")
+    raise
 
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
+def process_input(image, video, text_prompt, temperature, top_p, repetition_penalty, max_tokens, role, custom_role_text):
+    """Process the input and generate a response."""
+    try:
+        # Use custom role text if role is "Custom Role"
+        role_prompt = custom_role_text if role == "Custom Role" else ROLES[role]
+        
+        messages = [
+            {"role": "system", "content": role_prompt},
+            {"role": "user", "content": []}
+        ]
+        
+        # Add text prompt
+        if text_prompt:
+            messages[1]["content"].append({"type": "text", "text": text_prompt})
+        
+        # Add image if provided
+        if image is not None:
+            messages[1]["content"].append({"type": "image", "image": image})
+        
+        # Add video if provided
+        if video is not None:
+            messages[1]["content"].append({
+                "type": "video",
+                "video": video,
+                "fps": 4,
+            })
+        
+        # Process the prompt
+        prompt = processor.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True,
+        )
+        
+        # Process vision information
+        image_inputs, video_inputs, video_kwargs = process_vision_info(messages, return_video_kwargs=True)
+        
+        # Prepare inputs
+        inputs = processor(
+            text=prompt,
+            images=image_inputs if image_inputs is not None else None,
+            videos=video_inputs if video_inputs is not None else None,
+            return_tensors="pt"
+        ).to(model.device)
+        
+        # Update generation config with user parameters
+        current_generation_config = {
+            "temperature": temperature,
+            "top_p": top_p,
+            "repetition_penalty": repetition_penalty,
+            "max_new_tokens": max_tokens,
+        }
+        
+        # Generate response
+        with torch.no_grad():
+            outputs = model.generate(
+                **inputs,
+                **current_generation_config
+            )
+        
+        generated_text = processor.decode(outputs[0], skip_special_tokens=True)
+        
+        return generated_text, "✅ Generation completed successfully!"
+    except Exception as e:
+        import traceback
+        error_trace = traceback.format_exc()
+        return f"Error processing input: {str(e)}", f"❌ Error occurred:\n{error_trace}"
 
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
+def apply_config_changes(attention_mode, torch_dtype, device_map):
+    """Apply configuration changes and save to file."""
+    try:
+        config = {
+            "attention_mode": attention_mode,
+            "torch_dtype": torch_dtype,
+            "device_map": device_map,
+            "trust_remote_code": True
+        }
+        
+        with open(config_file, "w") as f:
+            json.dump(config, f, indent=4)
+        
+        return "Configuration updated. Please restart the application for changes to take effect."
+    except Exception as e:
+        return f"Error updating configuration: {str(e)}"
 
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
+# Create the Gradio interface
+with gr.Blocks(title="Cosmos-Reason1", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# Cosmos-Reason1")
+    gr.Markdown("Upload an image or video and ask a question about it.")
+    
+    with gr.Accordion("Model Configuration", open=False):
+        attention_mode = gr.Dropdown(
+            choices=["sdpa", "xformers", "flash_attention_2"],
+            value=config["attention_mode"],
+            label="Attention Mode"
+        )
+        torch_dtype = gr.Dropdown(
+            choices=["float16", "bfloat16", "float32"],
+            value=config["torch_dtype"],
+            label="Torch Data Type"
+        )
+        device_map = gr.Dropdown(
+            choices=["auto", "cuda", "cpu"],
+            value=config["device_map"],
+            label="Device Map"
+        )
+        config_btn = gr.Button("Apply Configuration")
+        config_msg = gr.Markdown()
+        
+        config_btn.click(
+            fn=apply_config_changes,
+            inputs=[attention_mode, torch_dtype, device_map],
+            outputs=config_msg
+        )
+    
+    with gr.Row():
+        with gr.Column():
+            role_selector = gr.Dropdown(
+                choices=list(ROLES.keys()),
+                value="General Assistant",
+                label="Select Role"
+            )
+            
+            custom_role_panel = gr.Group(visible=False)
+            with custom_role_panel:
+                custom_role_text = gr.Textbox(
+                    label="Custom Role Instructions",
+                    placeholder="Enter custom role instructions here...",
+                    lines=10,
+                    value=ROLES["Custom Role"]
                 )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
+                apply_custom_role = gr.Button("Apply Custom Role")
+                custom_role_status = gr.Markdown()
+                
+                def update_custom_role(text):
+                    ROLES["Custom Role"] = text
+                    return "Custom role updated successfully!"
+                
+                apply_custom_role.click(
+                    fn=update_custom_role,
+                    inputs=[custom_role_text],
+                    outputs=[custom_role_status]
                 )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
+            
+            def toggle_custom_role(role):
+                return gr.update(visible=(role == "Custom Role"))
+            
+            role_selector.change(
+                fn=toggle_custom_role,
+                inputs=[role_selector],
+                outputs=[custom_role_panel]
+            )
+            
+            image_input = gr.Image(label="Image Input", type="filepath")
+            video_input = gr.Video(label="Video Input")
+            text_input = gr.Textbox(label="Question", placeholder="Ask a question about the image or video...")
+            
+            with gr.Accordion("Generation Parameters", open=False):
+                temperature = gr.Slider(0.1, 2.0, value=0.6, step=0.1, label="Temperature")
+                top_p = gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top P")
+                repetition_penalty = gr.Slider(1.0, 2.0, value=1.05, step=0.05, label="Repetition Penalty")
+                max_tokens = gr.Slider(64, 4096, value=4096, step=64, label="Max Tokens")
+            
+            submit_btn = gr.Button("Submit")
+        
+        with gr.Column():
+            output = gr.Textbox(label="Model Response", lines=10)
+            status = gr.Markdown(label="Status")
+    
+    submit_btn.click(
+        fn=process_input,
         inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
+            image_input,
+            video_input,
+            text_input,
+            temperature,
+            top_p,
+            repetition_penalty,
+            max_tokens,
+            role_selector,
+            custom_role_text
         ],
-        outputs=[result, seed],
+        outputs=[output, status]
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()