other version

README.md

@@ -3,9 +3,9 @@ title: BoltzGen
 emoji: 📈
 colorFrom: red
 colorTo: indigo
-sdk: docker
+sdk: gradio
+sdk_version: 6.3.0 
 pinned: true
-app_port: 7680
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,90 +1,65 @@
 import gradio as gr
-#import torch
-from pathlib import Path
 import os
-import sys
-
-if "/home/user/app/BoltzGen" not in sys.path:
-    sys.path.append("/home/user/app/BoltzGen")
-    
-from boltzgen import boltzgen
+import subprocess
+import yaml
+from pathlib import Path
 
-# Use HF cache dir from env
-CACHE_DIR = os.environ.get("HF_HOME", "/cache")
+# Example YAML from README style (customize as needed)
+EXAMPLE_YAML = """
+entities:
+  - protein:
+      id: B
+      sequence: 80..140
+  - file:
+      path: 6m1u.cif  # Upload your target PDB/CIF
+      include:
+        - chain:
+            id: A
+"""
 
-def generate_protein(sequence_prompt, num_designs=4, temperature=0.8):
-    """
-    Generate protein designs using BoltzGen from a text prompt.
-    """
-    try:
-        # Initialize BoltzGen generator (assumes models loaded from HF cache)
-        generator = boltzgen.Generator(model_dir=CACHE_DIR)
-        
-        # Generate designs
-        designs = generator.generate(
-            prompt=sequence_prompt,
-            num_designs=num_designs,
-            temperature=temperature,
-            num_inference_steps=50
-        )
-        
-        # Format results for display
-        results = []
-        for i, design in enumerate(designs):
-            result = f"**Design {i+1}:**\n```fasta\n{design.sequence}\n```\n"
-            result += f"**Score:** {design.score:.3f}\n"
-            result += f"**Length:** {len(design.sequence)}\n"
-            if hasattr(design, 'pdb') and design.pdb:
-                result += f"[PDB structure available]\n"
-            results.append(result)
-        
-        return "\n---\n".join(results)
+def run_boltzgen(yaml_content, protocol="protein-anything", num_designs=10, budget=2, output_dir="./output"):
+    os.makedirs(output_dir, exist_ok=True)
+    
+    # Write YAML spec
+    yaml_path = Path(output_dir) / "design.yaml"
+    with open(yaml_path, "w") as f:
+        yaml.dump(yaml.safe_load(yaml_content), f)
     
+    # Run BoltzGen (models auto-download to ~/.cache/huggingface)
+    cmd = [
+        "boltzgen", "run", str(yaml_path),
+        "--output", output_dir,
+        "--protocol", protocol,
+        "--num_designs", str(num_designs),
+        "--budget", str(budget),
+        "--cache", "/tmp/cache"  # Persistent cache in Space
+    ]
+    
+    try:
+        result = subprocess.run(cmd, capture_output=True, text=True, cwd=output_dir)
+        if result.returncode == 0:
+            return f"Success! Designs saved to {output_dir}/final_ranked_designs/. Logs:\n{result.stdout}"
+        else:
+            return f"Error:\n{result.stderr}"
     except Exception as e:
-        return f"Error generating design: {str(e)}"
+        return f"Failed: {str(e)}"
 
-# Gradio Interface
-def create_demo():
-    with gr.Blocks(
-        title="BoltzGen Protein Design",
-        theme=gr.themes.Soft()
-    ) as demo:
-        gr.Markdown("# 🧬 BoltzGen Protein Generator")
-        gr.Markdown("Generate novel protein sequences from natural language prompts using BoltzGen.")
-        
-        with gr.Row():
-            with gr.Column(scale=1):
-                prompt = gr.Textbox(
-                    label="Protein Design Prompt",
-                    placeholder="Design a stable alpha-helical bundle with 100 residues...",
-                    lines=3,
-                    value="Design a stable TIM barrel enzyme scaffold"
-                )
-                num_designs = gr.Slider(1, 8, value=4, label="Number of Designs")
-                temperature = gr.Slider(0.1, 2.0, value=0.8, label="Temperature (Creativity)")
-                generate_btn = gr.Button("Generate Proteins", variant="primary")
-            
-            with gr.Column(scale=1):
-                output = gr.Markdown(label="Generated Designs")
-        
-        generate_btn.click(
-            fn=generate_protein,
-            inputs=[prompt, num_designs, temperature],
-            outputs=output
-        )
-        
-        gr.Markdown("### Examples")
-        gr.Examples(
-            examples=[
-                ["Design a stable alpha-helical bundle with 100 residues"],
-                ["Create a beta-barrel protein for membrane insertion"],
-                ["Generate a de novo enzyme scaffold with catalytic triad"]
-            ],
-            inputs=[prompt]
-        )
+# Gradio interface
+with gr.Blocks(title="BoltzGen on HF Spaces") as demo:
+    gr.Markdown("# BoltzGen: Protein Binder Design\nRun designs directly in-browser with GPU.")
+    
+    with gr.Row():
+        yaml_input = gr.Textbox(EXAMPLE_YAML, label="Design YAML", lines=15)
+        protocol = gr.Dropdown(["protein-anything", "peptide-anything", "nanobody-anything"], value="protein-anything", label="Protocol")
+    
+    with gr.Row():
+        num_designs = gr.Slider(10, 1000, value=50, label="Num Designs (start small)")
+        budget = gr.Slider(1, 20, value=2, label="Final Budget")
+    
+    run_btn = gr.Button("Run BoltzGen", variant="primary")
+    output = gr.Textbox(label="Output Logs & Results", lines=10)
     
-    return demo
+    run_btn.click(run_boltzgen, inputs=[yaml_input, protocol, num_designs, budget], outputs=output)
 
 if __name__ == "__main__":
-    demo = create_demo()
-    demo.launch(server_name="0.0.0.0", server_port=7860)
+    demo.launch()

requirements.txt

@@ -1,6 +1,14 @@
-torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
-gradio
-transformers
-accelerate
+torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu124
+boltzgen
+huggingface_hub
+pyyaml
 omegaconf
+hydra-core
+lightning
 einops
+se3diffusers
+e3nn==0.5.3
+diffusers
+transformers
+accelerate
+