Duplicate from amatiger/GODEL-Demo

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README.md

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+---
+title: GODEL Demo
+emoji: 🐠
+colorFrom: yellow
+colorTo: blue
+sdk: gradio
+sdk_version: 3.6
+app_file: app.py
+pinned: false
+license: mit
+duplicated_from: amatiger/GODEL-Demo
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+
+from transformers import (
+    AutoTokenizer,
+    AutoModel,
+    AutoModelForSeq2SeqLM,
+    AutoModelForCausalLM
+)
+
+tokenizer = AutoTokenizer.from_pretrained("microsoft/GODEL-v1_1-base-seq2seq")
+model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/GODEL-v1_1-base-seq2seq")
+
+preset_examples = [
+    ('Instruction: given a dialog context, you need to response empathically.',
+     '', 'Does money buy happiness?', 'Chitchat'),
+]
+
+
+def generate(instruction, knowledge, dialog, top_p, min_length, max_length):
+    if knowledge != '':
+        knowledge = '[KNOWLEDGE] ' + knowledge
+    dialog = ' EOS '.join(dialog)
+    query = f"{instruction} [CONTEXT] {dialog} {knowledge}"
+
+    input_ids = tokenizer(f"{query}", return_tensors="pt").input_ids
+    outputs = model.generate(input_ids, min_length=int(
+        min_length), max_length=int(max_length), top_p=top_p, do_sample=True)
+    output = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    print(query)
+    print(output)
+    return output
+
+
+def api_call_generation(instruction, knowledge, query, top_p, min_length, max_length):
+
+    dialog = [
+        query
+    ]
+    response = generate(instruction, knowledge, dialog,
+                        top_p, min_length, max_length)
+
+    return response
+
+
+def change_example(choice):
+    choice_idx = int(choice.split()[-1]) - 1
+    instruction, knowledge, query, instruction_type = preset_examples[choice_idx]
+    return [gr.update(lines=1, visible=True, value=instruction), gr.update(visible=True, value=knowledge), gr.update(lines=1, visible=True, value=query), gr.update(visible=True, value=instruction_type)]
+
+def change_textbox(choice):
+    if choice == "Chitchat":
+        return gr.update(lines=1, visible=True, value="Instruction: given a dialog context, you need to response empathically.")
+    elif choice == "Grounded Response Generation":
+        return gr.update(lines=1, visible=True, value="Instruction: given a dialog context and related knowledge, you need to response safely based on the knowledge.")
+    else:
+        return gr.update(lines=1, visible=True, value="Instruction: given a dialog context and related knowledge, you need to answer the question based on the knowledge.")
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("# The broken God")
+    gr.Markdown('''All hail Mekhane. Reject flesh. Embrace metal''')
+
+    dropdown = gr.Dropdown(
+        [f"Example {i+1}" for i in range(1)], label='Examples')
+
+    radio = gr.Radio(
+        ["Conversational Question Answering", "Chitchat", "Grounded Response Generation"], label="Instruction Type", value='Conversational Question Answering'
+    )
+    instruction = gr.Textbox(lines=1, interactive=True, label="Instruction",
+                             value="Instruction: given a dialog context and related knowledge, you need to answer the question based on the knowledge.")
+    radio.change(fn=change_textbox, inputs=radio, outputs=instruction)
+    knowledge = gr.Textbox(lines=6, label="Knowledge")
+    query = gr.Textbox(lines=1, label="User Query")
+
+    dropdown.change(change_example, dropdown, [instruction, knowledge, query, radio])
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            response = gr.Textbox(label="Response", lines=2)
+
+        with gr.Column(scale=1):
+            top_p = gr.Slider(0, 1, value=0.9, label='top_p')
+            min_length = gr.Number(8, label='min_length')
+            max_length = gr.Number(
+                64, label='max_length (should be larger than min_length)')
+
+    greet_btn = gr.Button("Generate")
+    greet_btn.click(fn=api_call_generation, inputs=[
+                    instruction, knowledge, query, top_p, min_length, max_length], outputs=response)
+
+demo.launch()

requirements.txt

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+torch==1.10.1
+transformers==4.22.2
+tokenizers==0.11.1
+pandas==1.5.0