Feat/load contract w abi

examples/agents/action_agent.py

@@ -7,18 +7,19 @@
 
 # Process the image
 def process_image(img):
-    img = np.resize(img, (28,28))
-    img = img.reshape(1,1,28,28)
-    img = img/255.0
+    img = np.resize(img, (28, 28))
+    img = img.reshape(1, 1, 28, 28)
+    img = img / 255.0
     print(img.shape)
     # For now, we will just use a small tensor as input to a single-layer softmax. We will change this when the PoC works
-    tensor = np.random.rand(1,3)
+    tensor = np.random.rand(1, 3)
     return tensor
-
+
+
 # Get the image
 def get_image(path):
     with Image.open(path) as img:
-        img = img.convert('L')
+        img = img.convert("L")
         img = np.array(img)
     return img
 
@@ -38,7 +39,7 @@ def transmission():
         id=id,
         chain="ethereum:sepolia:geth",
         version_id=version,
-        account=account
+        account=account,
     )
 
     result = agent.predict(input_feed={"image": img}, verifiable=True)
@@ -53,4 +54,5 @@ def transmission():
     pprint.pprint(contract_result.__dict__)
     logger.info("Finished")
 
-transmission()
+
+transmission()

examples/agents/read_contracts.py

@@ -29,7 +29,7 @@
     contracts={
         "mnist": "0x17807a00bE76716B91d5ba1232dd1647c4414912",
         "token": "0xeF7cCAE97ea69F5CdC89e496b0eDa2687C95D93B",
-        },
+    },
     chain="ethereum:sepolia:geth",
     account=ACCOUNT_ALIAS,
 )

examples/uni_v3_lp/action_agent.py

@@ -21,24 +21,23 @@
 VERSION_ID = ...  # Update with your version ID
 
 
-@task(name="Data processing")
+@task
 def process_data(realized_vol, dec_price_change):
     pct_change_sq = (100 * dec_price_change) ** 2
     X = np.array([[realized_vol, pct_change_sq]])
-
     return X
 
 
 # Get image
-@task(name="Get volatility and price change data")
+@task
 def get_data():
     # TODO: implement fetching onchain or from some other source
     realized_vol = 4.20
     dec_price_change = 0.1
     return realized_vol, dec_price_change
 
 
-@task(name="Create a Giza agent for the Volatility prediction model")
+@task
 def create_agent(
     model_id: int, version_id: int, chain: str, contracts: dict, account: str
 ):
@@ -55,7 +54,7 @@ def create_agent(
     return agent
 
 
-@task(name="Predict the digit in an image.")
+@task
 def predict(agent: GizaAgent, X: np.ndarray):
     """
     Predict the digit in an image.
@@ -70,7 +69,7 @@ def predict(agent: GizaAgent, X: np.ndarray):
     return prediction
 
 
-@task(name="Get the value from the prediction.")
+@task
 def get_pred_val(prediction: AgentResult):
     """
     Get the value from the prediction.
@@ -86,7 +85,7 @@ def get_pred_val(prediction: AgentResult):
 
 
 # Create Action
-@action(log_prints=True)
+@action
 def transmission(
     pred_model_id,
     pred_version_id,
@@ -111,9 +110,10 @@ def transmission(
     logger.info(f"Input data: {realized_vol}, {dec_price_change}")
     X = process_data(realized_vol, dec_price_change)
 
+    nft_manager_abi_path = "nft_manager_abi.json"
     contracts = {
-        "nft_manager": nft_manager_address,
-        "tokenA": tokenA_address,
+        "nft_manager": [nft_manager_address, nft_manager_abi_path],
+        "tokenA": [tokenA_address],
         "tokenB": tokenB_address,
         "pool": pool_address,
     }
@@ -147,7 +147,14 @@ def transmission(
             curr_tick, predicted_value, tokenA_decimals, tokenB_decimals, pool_fee
         )
         mint_params = get_mint_params(
-            user_address, tokenA_amount, tokenB_amount, pool_fee, lower_tick, upper_tick
+            tokenA_address,
+            tokenB_address,
+            user_address,
+            tokenA_amount,
+            tokenB_amount,
+            pool_fee,
+            lower_tick,
+            upper_tick,
         )
         # step 5: mint new position
         logger.info("Minting new position...")

examples/uni_v3_lp/mint_position.py

@@ -14,6 +14,8 @@
 
 def get_mint_params(
     user_address,
+    token0_address,
+    token1_address,
     amount0,
     amount1,
     pool_fee,
@@ -25,8 +27,8 @@ def get_mint_params(
     if deadline is None:
         deadline = int(time.time()) + 60
     mint_params = {
-        "token0": tokenA.address,
-        "token1": tokenB.address,
+        "token0": token0_address,
+        "token1": token1_address,
         "fee": pool_fee,
         "tickLower": lower_tick,
         "tickUpper": upper_tick,
@@ -74,44 +76,45 @@ def close_position(user_address, nft_manager, nft_id):
         nft_manager.collect((nft_id, user_address, MAX_UINT_128, MAX_UINT_128))
 
 
-networks.parse_network_choice(f"ethereum:sepolia:{sepolia_rpc_url}").__enter__()
-chain_id = chain.chain_id
+if __name__ == "__main__":
+    networks.parse_network_choice(f"ethereum:sepolia:{sepolia_rpc_url}").__enter__()
+    chain_id = chain.chain_id
 
-# step 1: set params
-tokenA_amount = 1000
-tokenB_amount = 1000
-pct_dev = 0.1
-pool_fee = 3000
-# step 2: load contracts
-tokenA = Contract(ADDRESSES["UNI"][chain_id])
-tokenB = Contract(ADDRESSES["WETH"][chain_id])
-nft_manager = Contract(ADDRESSES["NonfungiblePositionManager"][chain_id])
-pool_factory = Contract(ADDRESSES["PoolFactory"][chain_id])
-pool_address = pool_factory.getPool(tokenA.address, tokenB.address, pool_fee)
-pool = Contract(pool_address)
-dev = accounts.load("dev")
-dev.set_autosign(True, passphrase=dev_passphrase)
-user_address = dev.address
-with accounts.use_sender("dev"):
-    # step 3: fetch open positions
-    positions = get_all_user_positions(nft_manager, user_address)
-    print(f"Fouund the following open positions: {positions}")
-    # step 4: close all positions
-    print("Closing all open positions...")
-    for nft_id in positions:
-        close_position(user_address, nft_manager, nft_id)
-    # step 4: calculate mint params
-    print("Calculating mint params...")
-    _, curr_tick, _, _, _, _, _ = pool.slot0()
-    tokenA_decimals = tokenA.decimals()
-    tokenB_decimals = tokenB.decimals()
-    curr_price = tick_to_price(curr_tick, tokenA_decimals, tokenB_decimals)
-    lower_tick, upper_tick = get_tick_range(
-        curr_tick, pct_dev, tokenA_decimals, tokenB_decimals, pool_fee
-    )
-    mint_params = get_mint_params(
-        user_address, tokenA_amount, tokenB_amount, pool_fee, lower_tick, upper_tick
-    )
-    # step 5: mint new position
-    print("Minting new position...")
-    nft_manager.mint(mint_params)
+    # step 1: set params
+    tokenA_amount = 1000
+    tokenB_amount = 1000
+    pct_dev = 0.1
+    pool_fee = 3000
+    # step 2: load contracts
+    tokenA = Contract(ADDRESSES["UNI"][chain_id])
+    tokenB = Contract(ADDRESSES["WETH"][chain_id])
+    nft_manager = Contract(ADDRESSES["NonfungiblePositionManager"][chain_id])
+    pool_factory = Contract(ADDRESSES["PoolFactory"][chain_id])
+    pool_address = pool_factory.getPool(tokenA.address, tokenB.address, pool_fee)
+    pool = Contract(pool_address)
+    dev = accounts.load("dev")
+    dev.set_autosign(True, passphrase=dev_passphrase)
+    user_address = dev.address
+    with accounts.use_sender("dev"):
+        # step 3: fetch open positions
+        positions = get_all_user_positions(nft_manager, user_address)
+        print(f"Fouund the following open positions: {positions}")
+        # step 4: close all positions
+        print("Closing all open positions...")
+        for nft_id in positions:
+            close_position(user_address, nft_manager, nft_id)
+        # step 4: calculate mint params
+        print("Calculating mint params...")
+        _, curr_tick, _, _, _, _, _ = pool.slot0()
+        tokenA_decimals = tokenA.decimals()
+        tokenB_decimals = tokenB.decimals()
+        curr_price = tick_to_price(curr_tick, tokenA_decimals, tokenB_decimals)
+        lower_tick, upper_tick = get_tick_range(
+            curr_tick, pct_dev, tokenA_decimals, tokenB_decimals, pool_fee
+        )
+        mint_params = get_mint_params(
+            user_address, tokenA_amount, tokenB_amount, pool_fee, lower_tick, upper_tick
+        )
+        # step 5: mint new position
+        print("Minting new position...")
+        nft_manager.mint(mint_params)

examples/verifiable_mnist/deployments/pytorch_mnist_deployment.py

@@ -7,7 +7,7 @@
 from torch.utils.data import DataLoader, TensorDataset
 
 
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 input_size = 196  # 14x14
 hidden_size = 10
@@ -39,30 +39,29 @@ def resize_images(images):
 async def prepare_datasets():
     print("Prepare dataset ...")
 
-    train_dataset = torchvision.datasets.MNIST(
-        root='./data', train=True, download=True)
-    test_dataset = torchvision.datasets.MNIST(root='./data', train=False)
+    train_dataset = torchvision.datasets.MNIST(root="./data", train=True, download=True)
+    test_dataset = torchvision.datasets.MNIST(root="./data", train=False)
 
     x_train = resize_images(train_dataset)
     x_test = resize_images(test_dataset)
 
-    x_train = torch.tensor(x_train.reshape(-1, 14*14).astype('float32') / 255)
-    y_train = torch.tensor(
-        [label for _, label in train_dataset], dtype=torch.long)
+    x_train = torch.tensor(x_train.reshape(-1, 14 * 14).astype("float32") / 255)
+    y_train = torch.tensor([label for _, label in train_dataset], dtype=torch.long)
 
-    x_test = torch.tensor(x_test.reshape(-1, 14*14).astype('float32') / 255)
-    y_test = torch.tensor(
-        [label for _, label in test_dataset], dtype=torch.long)
+    x_test = torch.tensor(x_test.reshape(-1, 14 * 14).astype("float32") / 255)
+    y_test = torch.tensor([label for _, label in test_dataset], dtype=torch.long)
     return x_train, y_train, x_test, y_test
 
 
 async def create_data_loaders(x_train, y_train, x_test, y_test):
     print("Create data loaders ...")
 
-    train_loader = DataLoader(TensorDataset(
-        x_train, y_train), batch_size=batch_size, shuffle=True)
-    test_loader = DataLoader(TensorDataset(
-        x_test, y_test), batch_size=batch_size, shuffle=False)
+    train_loader = DataLoader(
+        TensorDataset(x_train, y_train), batch_size=batch_size, shuffle=True
+    )
+    test_loader = DataLoader(
+        TensorDataset(x_test, y_test), batch_size=batch_size, shuffle=False
+    )
     return train_loader, test_loader
 
 
@@ -75,7 +74,7 @@ async def train_model(train_loader):
 
     for epoch in range(num_epochs):
         for i, (images, labels) in enumerate(train_loader):
-            images = images.to(device).reshape(-1, 14*14)
+            images = images.to(device).reshape(-1, 14 * 14)
             labels = labels.to(device)
 
             outputs = model(images)
@@ -87,7 +86,8 @@ async def train_model(train_loader):
 
             if (i + 1) % 100 == 0:
                 print(
-                    f'Epoch [{epoch + 1}/{num_epochs}], Step [{i + 1}/{len(train_loader)}], Loss: {loss.item():.4f}')
+                    f"Epoch [{epoch + 1}/{num_epochs}], Step [{i + 1}/{len(train_loader)}], Loss: {loss.item():.4f}"
+                )
     return model
 
 
@@ -96,21 +96,20 @@ async def test_model(model, test_loader):
         n_correct = 0
         n_samples = 0
         for images, labels in test_loader:
-            images = images.to(device).reshape(-1, 14*14)
+            images = images.to(device).reshape(-1, 14 * 14)
             labels = labels.to(device)
             outputs = model(images)
             _, predicted = torch.max(outputs.data, 1)
             n_samples += labels.size(0)
             n_correct += (predicted == labels).sum().item()
 
         acc = 100.0 * n_correct / n_samples
-        print(f'Accuracy of the network on the 10000 test images: {acc} %')
+        print(f"Accuracy of the network on the 10000 test images: {acc} %")
 
 
 def execution():
     x_train, y_train, x_test, y_test = prepare_datasets()
-    train_loader, test_loader = create_data_loaders(
-        x_train, y_train, x_test, y_test)
+    train_loader, test_loader = create_data_loaders(x_train, y_train, x_test, y_test)
     model = train_model(train_loader)
     test_model(model, test_loader)