Update app.py

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
 # Load the tokenizer and model
-model_name = "mohamedemam/QA_GeneraTor"
+model_name = "mohamedemam/QA_GeneraToR"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
 
@@ -44,7 +44,9 @@ def generate_qa(context, recommended_word, temperature, top_p, num_samples=3):
     )
     
     generated_text = tokenizer.batch_decode(output, skip_special_tokens=True)
-    return generated_text
+
+    formatted_output = "\n\n".join([f"Original Context: {context}", "Generated Sentences:"] + generated_text)
+    return formatted_output
 
 # Create the Gradio interface with sliders for temperature and top-p
 iface = gr.Interface(
@@ -52,13 +54,13 @@ iface = gr.Interface(
     inputs=[
         gr.inputs.Dropdown(example_contexts, label="Choose an Example"),
         gr.inputs.Radio(recommended_words, label="Choose a Recommended Word"),
-        gr.inputs.Slider(minimum=0.0, maximum=2, default=2.1, step=0.1, label="Temperature"),
-        gr.inputs.Slider(minimum=0.0, maximum=1, default=0.5, step=0.1, label="Top-p")
+        gr.inputs.Slider(minimum=0.0, maximum=2, default=2.1, step=0.01, label="Temperature"),
+        gr.inputs.Slider(minimum=0.0, maximum=1, default=0.5, step=0.01, label="Top-p")
     ],
-    outputs=gr.outputs.Table(columns=["Generated Sentences"]),
+    outputs=gr.outputs.Textbox(label="Generated Output"),
     title="Question Generation and Answering",
     description="Select an example context, choose a recommended word, adjust temperature and top-p. The model will generate questions and answers.",
 )
 
 # Launch the interface
-iface.launch()
+iface.launch(shring=True)