Update app.py

app.py

@@ -1,4 +1,4 @@
-"""import gradio as gr
+import gradio as gr
 import torch
 import time
 from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
@@ -10,7 +10,7 @@ model_name = "large-traversaal/Phi-4-Hindi"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
 print("Model and tokenizer loaded successfully!")
-option_mapping = {"translation": "### TRANSLATION ###", "mcq": "### MCQ ###", "nli": "### NLI ###", "summarization": "### SUMMARIZATION ###",
+option_mapping = {"translation": "### TRANSLATION ###", "mcq": "### MCQ ###", "nli": "### NLI ###", "summarization": "### SUMMARIZATION ###", "Boolean": "### BOOLEAN ###",
     "long response": "### LONG RESPONSE ###", "direct response": "### DIRECT RESPONSE ###", "paraphrase": "### PARAPHRASE ###", "code": "### CODE ###"}
 def generate_response(message, temperature, max_new_tokens, top_p, task):
     append_text = option_mapping.get(task, "")
@@ -41,7 +41,7 @@ with gr.Blocks() as demo:
     with gr.Row():
         with gr.Column():
             input_text = gr.Textbox(label="Input", placeholder="Enter your text here...", lines=5)
-            task_dropdown = gr.Dropdown(choices=["translation", "mcq", "nli", "summarization", "long response", "direct response", "paraphrase", "code"], value="long response", label="Task")
+            task_dropdown = gr.Dropdown(choices=["boolean", "translation", "mcq", "nli", "summarization", "long response", "direct response", "paraphrase", "code"], value="long response", label="Task")
             with gr.Row():
                 with gr.Column():
                     temperature = gr.Slider(minimum=0.0, maximum=1.0, value=0.1, step=0.01, label="Temperature")
@@ -111,4 +111,4 @@ with gr.Blocks(theme='1024m/1024m-1') as demo:
     send_btn.click(fn=generate_response, inputs=[input_text, temperature, max_new_tokens, top_p, task_dropdown], outputs=output_text)
     clear_btn.click(fn=lambda: ("", ""), inputs=None, outputs=[input_text, output_text])
 if __name__ == "__main__":
-    demo.queue().launch()
+    demo.queue().launch()"""