Update app.py

app.py

@@ -1,19 +1,30 @@
-from transformers import AutoTokenizer, AutoModel
+from transformers import AutoModel
 import gradio as gr
+import fasttext
+import torch
+from huggingface_hub import hf_hub_download
+
+repo_id = "tevykuch/zeroshot-reptile"
+filename = "metalearn_wordy.bin"
+model_path = hf_hub_download(repo_id=repo_id, filename=filename)
+
+fasttext_model = fasttext.load_model(model_path)
+model = AutoModel.from_pretrained(repo_id)
+
 
 def predict(input_text):
-    inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True, max_length=512)
+    words = input_text.split()   
+    embeddings = torch.tensor([fasttext_model.get_word_vector(word) for word in words])
+   
+    avg_embedding = embeddings.mean(dim=0).unsqueeze(0) 
+
     with torch.no_grad():
-        outputs = model(**inputs)
-        
-    predictions = torch.softmax(outputs.logits, dim=-1)
-    predicted_class = predictions.argmax().item()
-    
+        output = model(avg_embedding)
+    predicted_class = output.argmax(dim=1).item()
     return f"Predicted class: {predicted_class}"
 
-
 iface = gr.Interface(fn=predict, 
-                     inputs=gr.inputs.Textbox(lines=2, placeholder="Type your text here..."), 
+                     inputs="text", 
                      outputs="text",
                      title="My Model Demo",
                      description="Enter some text to see the model prediction.")