Update App.py

App.py

@@ -1,48 +1,35 @@
-import gradio as gr
-import tensorflow as tf
-from transformers import BertTokenizer, TFBertModel
-import numpy as np
-
-# Load your model
-model = tf.keras.models.load_model('models/model_files')
-
-# Load tokenizer
-tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-
-def preprocess_text(text):
-    inputs = tokenizer(text, return_tensors='tf', padding=True, truncation=True, max_length=512)
-    return inputs
-
-def predict(text, image, structured):
-    text_inputs = preprocess_text(text)
-    image = tf.image.resize(image, (224, 224))
-    image = tf.keras.applications.resnet50.preprocess_input(image)
-    structured = (structured - structured.mean()) / structured.std()
-    prediction = model.predict([text_inputs['input_ids'], text_inputs['attention_mask'], image, structured])
-    return prediction[0][0]
-
-# Define the chat function
-def chat_response(user_input):
-    return f"Model response to: {user_input}"
-
-# Define the code execution function
-def execute_code(code):
-    exec_globals = {}
-    exec(code, exec_globals)
-    return exec_globals.get("output", "No output")
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        with gr.Column():
-            chat_input = gr.Textbox(lines=2, placeholder="Enter your message here...")
-            chat_output = gr.Textbox(lines=5, placeholder="Model response will appear here...")
-            chat_button = gr.Button("Send")
-        with gr.Column():
-            code_input = gr.Textbox(lines=10, placeholder="Enter your code here...")
-            code_output = gr.Textbox(lines=5, placeholder="Code output will appear here...")
-            code_button = gr.Button("Run Code")
-    
-    chat_button.click(chat_response, inputs=chat_input, outputs=chat_output)
-    code_button.click(execute_code, inputs=code_input, outputs=code_output)
-
-demo.launch()
+import torch
+import torch.optim as optim
+from torch.utils.data import DataLoader
+from models.moe_model import MoEModel
+from utils.data_loader import load_data
+
+# Load data
+train_loader, test_loader = load_data()
+
+# Initialize model, loss function, and optimizer
+model = MoEModel(input_dim=512, num_experts=3)
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.Adam(model.parameters(), lr=0.001)
+
+# Training loop
+for epoch in range(10):
+    model.train()
+    for vision_input, audio_input, sensor_input, labels in train_loader:
+        optimizer.zero_grad()
+        outputs = model(vision_input, audio_input, sensor_input)
+        loss = criterion(outputs, labels)
+        loss.backward()
+        optimizer.step()
+    print(f"Epoch {epoch+1}, Loss: {loss.item()}")
+
+# Evaluation
+model.eval()
+correct, total = 0, 0
+with torch.no_grad():
+    for vision_input, audio_input, sensor_input, labels in test_loader:
+        outputs = model(vision_input, audio_input, sensor_input)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+print(f"Accuracy: {100 * correct / total}%")