initial push

.gitignore

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+# Virtual environments
+venv

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+import torch._dynamo
+torch._dynamo.config.suppress_errors = True
+
+# Load the model and tokenizer
+model_id = "answerdotai/ModernBERT-base"  # Replace with your conversational model if needed
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForMaskedLM.from_pretrained(model_id)
+
+# Function for conversation
+def conversation(input_text):
+    # Prepare the input text with a [MASK] token for a masked language model
+    inputs = tokenizer(input_text, return_tensors="pt")
+    
+    # Generate predictions
+    outputs = model(**inputs)
+    
+    masked_index = inputs["input_ids"][0].tolist().index(tokenizer.mask_token_id)
+    predicted_token_id = outputs.logits[0, masked_index].argmax(axis=-1)
+    predicted_token = tokenizer.decode(predicted_token_id)
+
+    return f"Predicted response: {predicted_token}"
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=conversation,
+    inputs=gr.Textbox(label="Enter your text (include [MASK]):"),
+    outputs=gr.Textbox(label="Predicted Response"),
+    title="Masked Language Model Conversation",
+    description="Type a sentence with [MASK] to predict the masked word using ModernBERT."
+)
+
+# Launch the interface
+interface.launch()

requirements.txt

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+transformers
+torch
+requests
+Pillow
+open_clip_torch
+diffusers
+transformers
+bloom
+# This is only needed for local deployment
+gradio