Create app.py

app.py

@@ -0,0 +1,59 @@
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("instruction-pretrain/instruction-synthesizer")
+tokenizer = AutoTokenizer.from_pretrained("instruction-pretrain/instruction-synthesizer")
+
+def parse_pred(pred):
+    """Extract the list of instruction-response pairs from the prediction"""
+    QA_str_list = pred.split('</END>')
+    if not pred.endswith('</END>'):
+        QA_str_list = QA_str_list[:-1]
+
+    QA_list = []
+    raw_questions = []
+    for QA_str in QA_str_list:
+        try:
+            assert len(QA_str.split('<ANS>')) == 2, f'invalid QA string: {QA_str}'
+            Q_str, A_str = QA_str.split('<ANS>')
+            Q_str, A_str = Q_str.strip(), A_str.strip()
+            assert Q_str.startswith('<QUE>'), f'invalid question string: {Q_str} in QA_str: {QA_str}'
+            assert len(A_str) > 0, f'invalid answer string in QA_str: {QA_str}'
+            Q_str = Q_str.replace('<QUE>', '').strip()
+            assert Q_str.lower() not in raw_questions, f'duplicate question: {Q_str}'
+            QA_list.append({'Q': Q_str, 'A': A_str})
+            raw_questions.append(Q_str.lower())
+        except:
+            pass
+
+    return QA_list
+
+
+def get_instruction_response_pairs(context):
+    '''Prompt the synthesizer to generate instruction-response pairs based on the given context'''
+    prompt = f'<s> <CON> {context} </CON>\n\n'
+    inputs = tokenizer(prompt, add_special_tokens=False, return_tensors="pt").input_ids.to(model.device)
+    outputs = model.generate(input_ids=inputs, max_new_tokens=400, do_sample=False)[0]
+
+    pred_start = int(inputs.shape[-1])
+    pred = tokenizer.decode(outputs[pred_start:], skip_special_tokens=True)
+    return parse_pred(pred)
+
+def generate_pairs(context):
+    instruction_response_pairs = get_instruction_response_pairs(context)
+    output = ""
+    for index, pair in enumerate(instruction_response_pairs):
+        output += f"## Instruction {index + 1}:\n{pair['Q']}\n## Response {index + 1}:\n{pair['A']}\n\n"
+    return output
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=generate_pairs,
+    inputs=gr.Textbox(lines=5, label="Enter context here"),
+    outputs=gr.Textbox(lines=20, label="Generated Instruction-Response Pairs"),
+    title="Instruction-Response Pair Generator",
+    description="Enter a context, and the model will generate relevant instruction-response pairs."
+)
+
+# Launch the interface
+iface.launch()