Update app.py

app.py

@@ -214,8 +214,6 @@
 # if __name__ == "__main__":
 #     main()
 
-
-
 # %%writefile app.py
 
 import streamlit as st
@@ -273,5 +271,182 @@ def train(net, trainloader, epochs):
             optimizer.step()
             optimizer.zero_grad()
 
-def test(net, testloader
+def test(net, testloader):
+    metric = load_metric("accuracy")
+    net.eval()
+    loss = 0
+    for batch in testloader:
+        batch = {k: v.to(DEVICE) for k, v in batch.items()}
+        with torch.no_grad():
+            outputs = net(**batch)
+        logits = outputs.logits
+        loss += outputs.loss.item()
+        predictions = torch.argmax(logits, dim=-1)
+        metric.add_batch(predictions=predictions, references=batch["labels"])
+    loss /= len(testloader)
+    accuracy = metric.compute()["accuracy"]
+    return loss, accuracy
+
+class CustomClient(fl.client.NumPyClient):
+    def __init__(self, net, trainloader, testloader, client_id):
+        self.net = net
+        self.trainloader = trainloader
+        self.testloader = testloader
+        self.client_id = client_id
+        self.losses = []
+        self.accuracies = []
+
+    def get_parameters(self, config):
+        return [val.cpu().numpy() for _, val in self.net.state_dict().items()]
+
+    def set_parameters(self, parameters):
+        params_dict = zip(self.net.state_dict().keys(), parameters)
+        state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})
+        self.net.load_state_dict(state_dict, strict=True)
+
+    def fit(self, parameters, config):
+        log(INFO, f"Client {self.client_id} is starting fit()")
+        self.set_parameters(parameters)
+        train(self.net, self.trainloader, epochs=1)
+        loss, accuracy = test(self.net, self.testloader)
+        self.losses.append(loss)
+        self.accuracies.append(accuracy)
+        log(INFO, f"Client {self.client_id} finished fit() with loss: {loss:.4f} and accuracy: {accuracy:.4f}")
+        return self.get_parameters(config={}), len(self.trainloader.dataset), {"loss": loss, "accuracy": accuracy}
+
+    def evaluate(self, parameters, config):
+        log(INFO, f"Client {self.client_id} is starting evaluate()")
+        self.set_parameters(parameters)
+        loss, accuracy = test(self.net, self.testloader)
+        log(INFO, f"Client {self.client_id} finished evaluate() with loss: {loss:.4f} and accuracy: {accuracy:.4f}")
+        return float(loss), len(self.testloader.dataset), {"accuracy": float(accuracy), "loss": float(loss)}
+
+    def plot_metrics(self, round_num, plot_placeholder):
+        if self.losses and self.accuracies:
+            plot_placeholder.write(f"#### Client {self.client_id} Metrics for Round {round_num}")
+            plot_placeholder.write(f"Loss: {self.losses[-1]:.4f}")
+            plot_placeholder.write(f"Accuracy: {self.accuracies[-1]:.4f}")
+
+            fig, ax1 = plt.subplots()
+
+            color = 'tab:red'
+            ax1.set_xlabel('Round')
+            ax1.set_ylabel('Loss', color=color)
+            ax1.plot(range(1, len(self.losses) + 1), self.losses, color=color)
+            ax1.tick_params(axis='y', labelcolor=color)
+
+            ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis
+            color = 'tab:blue'
+            ax2.set_ylabel('Accuracy', color=color)
+            ax2.plot(range(1, len(self.accuracies) + 1), self.accuracies, color=color)
+            ax2.tick_params(axis='y', labelcolor=color)
+
+            fig.tight_layout()
+            plot_placeholder.pyplot(fig)
+
+def read_log_file():
+    with open("log.txt", "r") as file:
+        return file.read()
+
+def main():
+    st.write("## Federated Learning with Dynamic Models and Datasets for Mobile Devices")
+    dataset_name = st.selectbox("Dataset", ["imdb", "amazon_polarity", "ag_news"])
+    model_name = st.selectbox("Model", ["bert-base-uncased", "facebook/hubert-base-ls960", "distilbert-base-uncased"])
+
+    NUM_CLIENTS = st.slider("Number of Clients", min_value=1, max_value=10, value=2)
+    NUM_ROUNDS = st.slider("Number of Rounds", min_value=1, max_value=10, value=3)
+
+    train_datasets, test_datasets, data_collator, raw_datasets = load_data(dataset_name, num_clients=NUM_CLIENTS)
+
+    trainloaders = []
+    testloaders = []
+    clients = []
+
+    for i in range(NUM_CLIENTS):
+        st.write(f"### Client {i+1} Datasets")
+
+        train_df = pd.DataFrame(train_datasets[i])
+        test_df = pd.DataFrame(test_datasets[i])
+
+        st.write("#### Train Dataset (Words)")
+        st.dataframe(raw_datasets["train"].select(random.sample(range(len(raw_datasets["train"])), 20)))
+        st.write("#### Train Dataset (Tokens)")
+        edited_train_df = st.data_editor(train_df, key=f"train_{i}")
+
+        st.write("#### Test Dataset (Words)")
+        st.dataframe(raw_datasets["test"].select(random.sample(range(len(raw_datasets["test"])), 20)))
+        st.write("#### Test Dataset (Tokens)")
+        edited_test_df = st.data_editor(test_df, key=f"test_{i}")
+
+        edited_train_dataset = Dataset.from_pandas(edited_train_df)
+        edited_test_dataset = Dataset.from_pandas(edited_test_df)
+
+        trainloader = DataLoader(edited_train_dataset, shuffle=True, batch_size=32, collate_fn=data_collator)
+        testloader = DataLoader(edited_test_dataset, batch_size=32, collate_fn=data_collator)
+
+        trainloaders.append(trainloader)
+        testloaders.append(testloader)
+
+        net = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2).to(DEVICE)
+        client = CustomClient(net, trainloader, testloader, client_id=i+1)
+        clients.append(client)
+
+    if st.button("Start Training"):
+        def client_fn(cid):
+            return clients[int(cid)]
+
+        def weighted_average(metrics):
+            accuracies = [num_examples * m["accuracy"] for num_examples, m in metrics]
+            losses = [num_examples * m["loss"] for num_examples, m in metrics]
+            examples = [num_examples for num_examples, _ in metrics]
+            return {"accuracy": sum(accuracies) / sum(examples), "loss": sum(losses) / sum(examples)}
+
+        strategy = fl.server.strategy.FedAvg(
+            fraction_fit=1.0,
+            fraction_evaluate=1.0,
+            evaluate_metrics_aggregation_fn=weighted_average,
+        )
+
+        for round_num in range(NUM_ROUNDS):
+            st.write(f"### Round {round_num + 1}")
+            plot_placeholders = [st.empty() for _ in range(NUM_CLIENTS)]
+
+            fl.simulation.start_simulation(
+                client_fn=client_fn,
+                num_clients=NUM_CLIENTS,
+                config=fl.server.ServerConfig(num_rounds=1),
+                strategy=strategy,
+                client_resources={"num_cpus": 1, "num_gpus": 0},
+                ray_init_args={"log_to_driver": False, "num_cpus": 1, "num_gpus": 0}
+            )
+
+            for i, client in enumerate(clients):
+                client.plot_metrics(round_num + 1, plot_placeholders[i])
+                st.write(" ")
+
+        st.success("Training completed successfully!")
+
+        # Display final metrics
+        st.write("## Final Client Metrics")
+        for client in clients:
+            st.write(f"### Client {client.client_id}")
+            if client.losses and client.accuracies:
+                st.write(f"Final Loss: {client.losses[-1]:.4f}")
+                st.write(f"Final Accuracy: {client.accuracies[-1]:.4f}")
+                client.plot_metrics(NUM_ROUNDS, st.empty())
+            else:
+                st.write("No metrics available.")
+
+            st.write(" ")
+
+        # Display log.txt content
+        st.write("## Training Log")
+        st.text(read_log_file())
+
+    else:
+        st.write("Click the 'Start Training' button to start the training process.")
+
+if __name__ == "__main__":
+    main()
+