Update app.py

app.py

@@ -1,3 +1,49 @@
 import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-gr.Interface.load("models/legacy107/flan-t5-large-bottleneck-adapter-cpgQA-unique").launch()
+# Load your fine-tuned model and tokenizer
+model_name = "legacy107/flan-t5-large-bottleneck-adapter-cpgQA-unique"
+tokenizer = AutoTokenizer.from_pretrained(model_name, device_map="auto")
+model = AutoModelForSeq2SeqLM.from_pretrained(
+    model_checkpoint, device_map="auto"
+)
+model.set_active_adapters("question_answering")
+
+max_length = 512
+max_target_length = 128
+
+# Define your function to generate answers
+def generate_answer(question, context):
+    # Combine question and context
+    input_text = f"question: {question} context: {context}"
+
+    # Tokenize the input text
+    input_ids = tokenizer(
+        input_text,
+        return_tensors="pt",
+        padding="max_length",
+        truncation=True,
+        max_length=512,
+    ).input_ids
+
+    # Generate the answer
+    with torch.no_grad():
+        generated_ids = model.generate(input_ids, max_new_tokens=max_target_length)
+
+    # Decode and return the generated answer
+    generated_answer = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+
+    return generated_answer
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=generate_answer,
+    inputs=[
+        gr.inputs.Textbox(label="Question"),
+        gr.inputs.Textbox(label="Context")
+    ],
+    outputs=gr.outputs.Textbox(label="Generated Answer")
+)
+
+# Launch the Gradio interface
+iface.launch()