Create app.py

app.py

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+import gradio as gr
+import torch
+from model import SimpleMultilingualClassifier  # Import your model
+
+# --- Configuration ---
+embedding_files = {
+    'en': 'fasttext_embeddings/cc.en.100.bin',
+    'fr': 'fasttext_embeddings/cc.fr.100.bin'
+    # Add more languages as needed
+}
+num_classes = 3  # Replace with the actual number of classes
+class_labels = ["positive", "negative", "neutral"] # Replace with your actual class labels
+
+# Load the model
+try:
+    model = SimpleMultilingualClassifier(embedding_files, num_classes)
+    # In a real scenario, you would load trained weights here:
+    # model.load_state_dict(torch.load('path/to/your/trained_weights.pth'))
+    model.eval()
+except Exception as e:
+    print(f"Error loading model: {e}")
+    model = None
+
+def classify_text(text, language):
+    if model:
+        try:
+            prediction = model.predict(text, language, class_labels)
+            return prediction
+        except ValueError as e:
+            return str(e)
+    else:
+        return "Model not loaded."
+
+iface = gr.Interface(
+    fn=classify_text,
+    inputs=[
+        gr.Textbox(label="Enter text"),
+        gr.Dropdown(choices=list(embedding_files.keys()), label="Language")
+    ],
+    outputs=gr.Textbox(label="Prediction"),
+    title="Simple Multilingual Text Classifier",
+    description="A basic multilingual text classifier using FastText embeddings.",
+)
+
+iface.launch()