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app.py

@@ -1,41 +1,57 @@
 import spaces
 import gradio as gr
 import torch
+import torch.nn as nn
+import random
 from rdkit import Chem
 from rdkit.Chem import Draw
-from graph_decoder.diffusion_model import GraphDiT
-
-# Load the model
-def load_graph_decoder(path='model_labeled'):
-    model = GraphDiT(
-        model_config_path=f"{path}/config.yaml",
-        data_info_path=f"{path}/data.meta.json",
-        model_dtype=torch.float32,
-    )
-    model.init_model(path)
-    model.disable_grads()
+
+class RandomPolymerGenerator(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.fc1 = nn.Linear(5, 64)
+        self.fc2 = nn.Linear(64, 128)
+        self.fc3 = nn.Linear(128, 256)
+        self.fc4 = nn.Linear(256, 100)  # Output size set to 100 for simplicity
+
+    def forward(self, x):
+        x = torch.relu(self.fc1(x))
+        x = torch.relu(self.fc2(x))
+        x = torch.relu(self.fc3(x))
+        return torch.sigmoid(self.fc4(x))
+
+def load_graph_decoder():
+    model = RandomPolymerGenerator()
     return model
 
-# model = load_graph_decoder()
-# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+ATOM_SYMBOLS = ['C', 'N', 'O', 'H']
+
+def generate_random_smiles(length=10):
+    return ''.join(random.choices(ATOM_SYMBOLS, k=length))
 
 @spaces.GPU
 def generate_polymer(CH4, CO2, H2, N2, O2, guidance_scale):
-    properties = [CH4, CO2, H2, N2, O2]
+    properties = torch.tensor([CH4, CO2, H2, N2, O2], dtype=torch.float32).unsqueeze(0)
     
+    print('in generate_polymer')
     try:
         model = load_graph_decoder()
         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
         model.to(device)
-        print('enter function')
-        generated_molecule, _ = model.generate(properties, device=device, guide_scale=guidance_scale)
+        properties = properties.to(device)
         
-        if generated_molecule is not None:
-            mol = Chem.MolFromSmiles(generated_molecule)
-            if mol is not None:
-                standardized_smiles = Chem.MolToSmiles(mol, isomericSmiles=True)
-                img = Draw.MolToImage(mol)
-                return standardized_smiles, img
+        with torch.no_grad():
+            output = model(properties)
+        print('output', output)
+        
+        # Generate a random SMILES string (this is a placeholder)
+        generated_molecule = generate_random_smiles()
+        
+        mol = Chem.MolFromSmiles(generated_molecule)
+        if mol is not None:
+            standardized_smiles = Chem.MolToSmiles(mol, isomericSmiles=True)
+            img = Draw.MolToImage(mol)
+            return standardized_smiles, img
     except Exception as e:
         print(f"Error in generation: {e}")
     
@@ -43,7 +59,7 @@ def generate_polymer(CH4, CO2, H2, N2, O2, guidance_scale):
 
 # Create the Gradio interface
 with gr.Blocks(title="Simplified Polymer Design") as iface:
-    gr.Markdown("## Polymer Design with GraphDiT")
+    gr.Markdown("## Polymer Design with Random Neural Network")
     
     with gr.Row():
         CH4_input = gr.Slider(0, 100, value=2.5, label="CH₄ (Barrier)")
@@ -66,4 +82,74 @@ with gr.Blocks(title="Simplified Polymer Design") as iface:
     )
 
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch()
+
+# import spaces
+# import gradio as gr
+# import torch
+# from rdkit import Chem
+# from rdkit.Chem import Draw
+# # from graph_decoder.diffusion_model import GraphDiT
+
+# # Load the model
+# def load_graph_decoder(path='model_labeled'):
+#     model = GraphDiT(
+#         model_config_path=f"{path}/config.yaml",
+#         data_info_path=f"{path}/data.meta.json",
+#         model_dtype=torch.float32,
+#     )
+#     model.init_model(path)
+#     model.disable_grads()
+#     return model
+
+# # model = load_graph_decoder()
+# # device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# @spaces.GPU
+# def generate_polymer(CH4, CO2, H2, N2, O2, guidance_scale):
+#     properties = [CH4, CO2, H2, N2, O2]
+    
+#     try:
+#         model = load_graph_decoder()
+#         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+#         model.to(device)
+#         print('enter function')
+#         generated_molecule, _ = model.generate(properties, device=device, guide_scale=guidance_scale)
+        
+#         if generated_molecule is not None:
+#             mol = Chem.MolFromSmiles(generated_molecule)
+#             if mol is not None:
+#                 standardized_smiles = Chem.MolToSmiles(mol, isomericSmiles=True)
+#                 img = Draw.MolToImage(mol)
+#                 return standardized_smiles, img
+#     except Exception as e:
+#         print(f"Error in generation: {e}")
+    
+#     return "Generation failed", None
+
+# # Create the Gradio interface
+# with gr.Blocks(title="Simplified Polymer Design") as iface:
+#     gr.Markdown("## Polymer Design with GraphDiT")
+    
+#     with gr.Row():
+#         CH4_input = gr.Slider(0, 100, value=2.5, label="CH₄ (Barrier)")
+#         CO2_input = gr.Slider(0, 100, value=15.4, label="CO₂ (Barrier)")
+#         H2_input = gr.Slider(0, 100, value=21.0, label="H₂ (Barrier)")
+#         N2_input = gr.Slider(0, 100, value=1.5, label="N₂ (Barrier)")
+#         O2_input = gr.Slider(0, 100, value=2.8, label="O₂ (Barrier)")
+#         guidance_scale = gr.Slider(1, 3, value=2, label="Guidance Scale")
+
+#     generate_btn = gr.Button("Generate Polymer")
+
+#     with gr.Row():
+#         result_smiles = gr.Textbox(label="Generated SMILES")
+#         result_image = gr.Image(label="Molecule Visualization", type="pil")
+
+#     generate_btn.click(
+#         generate_polymer,
+#         inputs=[CH4_input, CO2_input, H2_input, N2_input, O2_input, guidance_scale],
+#         outputs=[result_smiles, result_image]
+#     )
+
+# if __name__ == "__main__":
+#     iface.launch()