Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Load the tokenizer and model
+model_name = "mohamedemam/QA_GeneraTor"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+# Function to generate questions and answers with configurable parameters
+def generate_qa(context, temperature, top_p):
+    input_text = f"Quation answer: {context}"
+    input_ids = ttokenizer(input_text,max_length=400,truncation=True,padding="max_length",return_tensors='pt')
+    
+    # Generate with configurable parameters
+    output = model.generate(
+        input_ids,
+        max_length=150,
+        num_return_sequences=1,
+        no_repeat_ngram_size=2,
+        temperature=temperature,
+        top_p=top_p
+    )
+    
+    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+    return generated_text
+
+# Create the Gradio interface with sliders for temperature and top-p
+iface = gr.Interface(
+    fn=generate_qa,
+    inputs=["text", gr.inputs.Slider(minimum=0.2, maximum=2, default=1, step=0.1, label="Temperature"),
+            gr.inputs.Slider(minimum=0.1, maximum=1, default=0.8, step=0.1, label="Top-p")],
+    outputs="text",
+    title="Question Generation and Answering",
+    description="Enter a context, adjust temperature and top-p, and the model will generate a question and answer.",
+)
+
+# Launch the interface
+iface.launch()