Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Load your model
+model_name = "CJHauser/PrisimAI-t5"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+def answer_question(context, question):
+    input_text = f"question: {question} context: {context}"
+    inputs = tokenizer.encode(input_text, return_tensors="pt", truncation=True)
+    outputs = model.generate(inputs, max_length=128)
+    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return answer
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# 🤖 PrisimAI Q&A\nAsk questions based on a given context.")
+    
+    with gr.Row():
+        context = gr.Textbox(label="Context", placeholder="Paste your reference text here...", lines=8)
+    
+    question = gr.Textbox(label="Your Question", placeholder="What do you want to know?")
+    answer = gr.Textbox(label="Answer", interactive=False)
+    
+    btn = gr.Button("Get Answer")
+    btn.click(fn=answer_question, inputs=[context, question], outputs=answer)
+
+demo.launch()