Update app.py

app.py

@@ -1,153 +1,161 @@
-import os
-import gradio as gr
-from gradio_molecule3d import Molecule3D
-import spaces
-import subprocess
-import glob
-
-
-# directory to store cached outputs
-CACHE_DIR = "gradio_cached_examples"
-
-
-reps =    [
-    {
-      "model": 0,
-      "chain": "",
-      "resname": "",
-      "style": "stick",
-      "color": "whiteCarbon",
-      "residue_range": "",
-      "around": 0,
-      "byres": False,
-      "visible": False
-    }
-  ]
-# Ensure the cache directory exists
-os.makedirs(CACHE_DIR, exist_ok=True)
-
-# Define example files and precomputed outputs
-example_fasta_files = [
-    f"cache_examples/boltz_0.fasta",
-    f"cache_examples/Armadillo_6.fasta",
-    f"cache_examples/Covid_3.fasta",
-    f"cache_examples/Malaria_2.fasta",
-    f"cache_examples/MITOCHONDRIAL_9.fasta",
-    f"cache_examples/Monkeypox_4.fasta",
-    f"cache_examples/Plasmodium_1.fasta",
-    f"cache_examples/PROTOCADHERIN_8.fasta",
-    f"cache_examples/Vault_5.fasta",
-    f"cache_examples/Zipper_7.fasta",
-]
-
-# matching `.pdb` files in the `CACHE_DIR`
-example_outputs = [
-    os.path.join(CACHE_DIR, os.path.basename(fasta_file).replace(".fasta", ".pdb"))
-    for fasta_file in example_fasta_files
-]
-
-# must load cached outputs
-def load_cached_example_outputs(fasta_file: str) -> str:
-    # Find the corresponding `.pdb` file
-    pdb_file = os.path.basename(fasta_file).replace(".fasta", ".pdb")
-    cached_pdb_path = os.path.join(CACHE_DIR, pdb_file)
-    if os.path.exists(cached_pdb_path):
-        return cached_pdb_path
-    else:
-        raise FileNotFoundError(f"Cached output not found for {pdb_file}")
-
-# handle example click
-def on_example_click(fasta_file: str) -> str:
-    return load_cached_example_outputs(fasta_file)
-
-# run predictions
-@spaces.GPU(duration=120)
-def predict(data,
-            accelerator="gpu", sampling_steps=50,
-            diffusion_samples=1):
-
-    print("Arguments passed to `predict` function:")
-    print(f"  data: {data}")
-    print(f"  accelerator: {accelerator}")
-    print(f"  sampling_steps: {sampling_steps}")
-    print(f"  diffusion_samples: {diffusion_samples}")
-    # we construct the base command
-    command = [
-        "boltz", "predict",
-        "--out_dir", "./",
-        "--accelerator", accelerator,
-        "--sampling_steps", str(sampling_steps),
-        "--diffusion_samples", str(diffusion_samples),
-        "--output_format", "pdb",
-    ]
-    command.extend(["--checkpoint", "./ckpt/boltz1.ckpt"])
-    command.append(data)
-    result = subprocess.run(command, capture_output=True, text=True)
-    if result.returncode == 0:
-        print("Prediction completed successfully...!")
-        print(f"Output saved to: {out_dir}")
-    else:
-        print("Prediction failed :(")
-        print("Error:", result.stderr)
-
-def run_prediction(input_file, accelerator, sampling_steps,
-                   diffusion_samples):
-    data = input_file.name
-    print("the data : ", data)
-    predict(
-        data=data,
-        accelerator=accelerator,
-        sampling_steps=sampling_steps,
-        diffusion_samples=diffusion_samples
-    )
-
-    # search for the latest .pdb file in the predictions folder
-    out_dir = "./"
-    search_path = os.path.join(out_dir, "boltz_results*/predictions/**/*.pdb")
-    pdb_files = glob.glob(search_path, recursive=True)
+# import os
+# import gradio as gr
+# from gradio_molecule3d import Molecule3D
+# import spaces
+# import subprocess
+# import glob
+
+
+# # directory to store cached outputs
+# CACHE_DIR = "gradio_cached_examples"
+
+
+# reps =    [
+#     {
+#       "model": 0,
+#       "chain": "",
+#       "resname": "",
+#       "style": "stick",
+#       "color": "whiteCarbon",
+#       "residue_range": "",
+#       "around": 0,
+#       "byres": False,
+#       "visible": False
+#     }
+#   ]
+# # Ensure the cache directory exists
+# os.makedirs(CACHE_DIR, exist_ok=True)
+
+# # Define example files and precomputed outputs
+# example_fasta_files = [
+#     f"cache_examples/boltz_0.fasta",
+#     f"cache_examples/Armadillo_6.fasta",
+#     f"cache_examples/Covid_3.fasta",
+#     f"cache_examples/Malaria_2.fasta",
+#     f"cache_examples/MITOCHONDRIAL_9.fasta",
+#     f"cache_examples/Monkeypox_4.fasta",
+#     f"cache_examples/Plasmodium_1.fasta",
+#     f"cache_examples/PROTOCADHERIN_8.fasta",
+#     f"cache_examples/Vault_5.fasta",
+#     f"cache_examples/Zipper_7.fasta",
+# ]
+
+# # matching `.pdb` files in the `CACHE_DIR`
+# example_outputs = [
+#     os.path.join(CACHE_DIR, os.path.basename(fasta_file).replace(".fasta", ".pdb"))
+#     for fasta_file in example_fasta_files
+# ]
+
+# # must load cached outputs
+# def load_cached_example_outputs(fasta_file: str) -> str:
+#     # Find the corresponding `.pdb` file
+#     pdb_file = os.path.basename(fasta_file).replace(".fasta", ".pdb")
+#     cached_pdb_path = os.path.join(CACHE_DIR, pdb_file)
+#     if os.path.exists(cached_pdb_path):
+#         return cached_pdb_path
+#     else:
+#         raise FileNotFoundError(f"Cached output not found for {pdb_file}")
+
+# # handle example click
+# def on_example_click(fasta_file: str) -> str:
+#     return load_cached_example_outputs(fasta_file)
+
+# # run predictions
+# @spaces.GPU(duration=120)
+# def predict(data,
+#             accelerator="gpu", sampling_steps=50,
+#             diffusion_samples=1):
+
+#     print("Arguments passed to `predict` function:")
+#     print(f"  data: {data}")
+#     print(f"  accelerator: {accelerator}")
+#     print(f"  sampling_steps: {sampling_steps}")
+#     print(f"  diffusion_samples: {diffusion_samples}")
+#     # we construct the base command
+#     command = [
+#         "boltz", "predict",
+#         "--out_dir", "./",
+#         "--accelerator", accelerator,
+#         "--sampling_steps", str(sampling_steps),
+#         "--diffusion_samples", str(diffusion_samples),
+#         "--output_format", "pdb",
+#     ]
+#     command.extend(["--checkpoint", "./ckpt/boltz1.ckpt"])
+#     command.append(data)
+#     result = subprocess.run(command, capture_output=True, text=True)
+#     if result.returncode == 0:
+#         print("Prediction completed successfully...!")
+#         print(f"Output saved to: {out_dir}")
+#     else:
+#         print("Prediction failed :(")
+#         print("Error:", result.stderr)
+
+# def run_prediction(input_file, accelerator, sampling_steps,
+#                    diffusion_samples):
+#     data = input_file.name
+#     print("the data : ", data)
+#     predict(
+#         data=data,
+#         accelerator=accelerator,
+#         sampling_steps=sampling_steps,
+#         diffusion_samples=diffusion_samples
+#     )
+
+#     # search for the latest .pdb file in the predictions folder
+#     out_dir = "./"
+#     search_path = os.path.join(out_dir, "boltz_results*/predictions/**/*.pdb")
+#     pdb_files = glob.glob(search_path, recursive=True)
     
-    if not pdb_files:
-        print("No .pdb files found in the predictions folder.")
-        return None
+#     if not pdb_files:
+#         print("No .pdb files found in the predictions folder.")
+#         return None
 
-    # some manual logic
-    # get the latest .pdb file based on modification time
-    latest_pdb_file = max(pdb_files, key=os.path.getmtime)
-    return latest_pdb_file
+#     # some manual logic
+#     # get the latest .pdb file based on modification time
+#     latest_pdb_file = max(pdb_files, key=os.path.getmtime)
+#     return latest_pdb_file
 
 
-with gr.Blocks() as demo:
-    gr.Markdown("# 🔬 Boltz-1: Democratizing Biomolecular Interaction Modeling 🧬")
+# with gr.Blocks() as demo:
+#     gr.Markdown("# 🔬 Boltz-1: Democratizing Biomolecular Interaction Modeling 🧬")
 
-    with gr.Row():
-        with gr.Column(scale=1):
-            inp = gr.File(label="Upload a .fasta File", file_types=[".fasta"])
+#     with gr.Row():
+#         with gr.Column(scale=1):
+#             inp = gr.File(label="Upload a .fasta File", file_types=[".fasta"])
 
-            with gr.Accordion("Advanced Settings", open=False):
-                accelerator = gr.Radio(choices=["gpu", "cpu"], value="gpu", label="Accelerator")
-                sampling_steps = gr.Slider(minimum=1, maximum=500, value=50, step=1, label="Sampling Steps")
-                diffusion_samples = gr.Slider(minimum=1, maximum=10, value=1, step=1, label="Diffusion Samples")
-            btn = gr.Button("Predict")
+#             with gr.Accordion("Advanced Settings", open=False):
+#                 accelerator = gr.Radio(choices=["gpu", "cpu"], value="gpu", label="Accelerator")
+#                 sampling_steps = gr.Slider(minimum=1, maximum=500, value=50, step=1, label="Sampling Steps")
+#                 diffusion_samples = gr.Slider(minimum=1, maximum=10, value=1, step=1, label="Diffusion Samples")
+#             btn = gr.Button("Predict")
             
-        with gr.Column(scale=3):
-            out = Molecule3D(label="Generated Molecule", reps=reps)
+#         with gr.Column(scale=3):
+#             out = Molecule3D(label="Generated Molecule", reps=reps)
 
         
 
-    btn.click(
-        run_prediction,
-        inputs=[inp, accelerator, sampling_steps, diffusion_samples],
-        outputs=out
-    )
-    gr.Examples(
-                examples=[[fasta_file] for fasta_file in example_fasta_files],
-                inputs=[inp],
-                outputs=out, 
-                fn=lambda fasta_file: on_example_click(fasta_file),
-                cache_examples=True
-            )
+#     btn.click(
+#         run_prediction,
+#         inputs=[inp, accelerator, sampling_steps, diffusion_samples],
+#         outputs=out
+#     )
+#     gr.Examples(
+#                 examples=[[fasta_file] for fasta_file in example_fasta_files],
+#                 inputs=[inp],
+#                 outputs=out, 
+#                 fn=lambda fasta_file: on_example_click(fasta_file),
+#                 cache_examples=True
+#             )
 
     
 
-if __name__ == "__main__":
-    demo.launch(share=True, debug=True)
+# if __name__ == "__main__":
+#     demo.launch(share=True, debug=True)
+
+import gradio as gr
+
+def greet(name):
+    return "Hello " + name + "!!"
+
+demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+demo.launch()