Update app.py

app.py

@@ -1,246 +1,25 @@
 import gradio as gr
-import os
-import sys
-import json
-import time
-import openai
-import pickle
-import argparse
-import requests
-from tqdm import tqdm
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaForCausalLM, LlamaTokenizer
-
-from fastchat.model import load_model, get_conversation_template, add_model_args
-
-
-openai.api_key = "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"
-
-def greet(name):
-    return "Hello " + name + "!!"
-
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
-
-# determinant vs. determiner
-# https://wikidiff.com/determiner/determinant
-ents_prompt = [
-    'Noun',
-    'Verb',
-    'Adjective',
-    'Adverb',
-    'Preposition/Subord',
-    'Coordinating Conjunction',
-    # 'Cardinal Number',
-    'Determiner',
-    'Noun Phrase',
-    'Verb Phrase',
-    'Adjective Phrase',
-    'Adverb Phrase',
-    'Preposition Phrase',
-    'Conjunction Phrase',
-    'Coordinate Phrase',
-    'Quantitave Phrase',
-    'Complex Nominal',
-    'Clause',
-    'Dependent Clause',
-    'Fragment Clause',
-    'T-unit',
-    'Complex T-unit',
-    # 'Fragment T-unit',
-]
-ents = ['NN', 'VB', 'JJ', 'RB', 'IN', 'CC', 'DT', 'NP', 'VP', 'ADJP', 'ADVP', 'PP', 'CONJP', 'CP', 'QP', 'CN', 'C', 'DC', 'FC', 'T', 'CT']
-
-
-model_mapping = {
-    # 'gpt3': 'gpt-3',
-    'gpt3.5': 'gpt-3.5-turbo-0613',
-    'vicuna-7b': 'lmsys/vicuna-7b-v1.3',
-    'vicuna-13b': 'lmsys/vicuna-13b-v1.3',
-    'vicuna-33b': 'lmsys/vicuna-33b-v1.3',
-    'fastchat-t5': 'lmsys/fastchat-t5-3b-v1.0',
-    # 'llama2': 'meta-llama/Llama-2-7b-chat-hf',
-    'llama-7b': '/data/jiali/llama/hf/7B',
-    'llama-13b': '/data/jiali/llama/hf/13B',
-    'llama-30b': '/data/jiali/llama/hf/30B',
-    'llama-65b': '/data/jiali/llama/hf/65B',
-    'alpaca': '/data/jiali/alpaca-7B',
-    # 'koala-7b': 'koala-7b',
-    # 'koala-13b': 'koala-13b',
-}
-
-for m in model_mapping.keys():
-    for eid, ent in enumerate(ents):
-        os.makedirs(f'result/openai_result/{m}/ptb/per_ent/{ent}', exist_ok=True)
-        os.makedirs(f'result/structured_prompt/{m}/ptb', exist_ok=True)
-
-
-# s = int(sys.argv[1])
-# e = int(sys.argv[2])
-
-s = 0
-e = 1000
-with open('ptb_corpus/sample_uniform_1k_2.txt', 'r') as f:
-    selected_idx = f.readlines()
-selected_idx = [int(i.strip()) for i in selected_idx][s:e]
-
-
-ptb = []
-with open('./ptb_corpus/ptb.jsonl', 'r') as f:
-    for l in f:
-        ptb.append(json.loads(l))
-
-
-## Prompt 1
-template_all = '''Please output the <Noun, Verb, Adjective, Adverb, Preposition/Subord, Coordinating Conjunction, Cardinal Number, Determiner, Noun Phrase, Verb Phrase, Adjective Phrase, Adverb Phrase, Preposition Phrase, Conjunction Phrase, Coordinate Phrase, Quantitave Phrase, Complex Nominal, Clause, Dependent Clause, Fragment Clause, T-unit, Complex T-unit, Fragment T-unit> in the following sentence without any additional text in json format: "{}"'''
-template_single = '''Please output any <{}> in the following sentence one per line without any additional text: "{}"'''
-
-## Prompt 2
-with open('ptb_corpus/structured_prompting_demonstration_42.txt', 'r') as f:
-    demonstration = f.read()
-
-
-def para(m):
-    c = 0
-    for n, p in m.named_parameters():
-        c += p.numel()
-    return c
-
-def main(args=None):
-
-    if 'gpt3' in args.model:
-        pass
-
-    else:
-        path = model_mapping[args.model]
-        model, tokenizer = load_model(
-            path,
-            args.device,
-            args.num_gpus,
-            args.max_gpu_memory,
-            args.load_8bit,
-            args.cpu_offloading,
-            revision=args.revision,
-            debug=args.debug,
-        )
-
-    if args.prompt == 1:
-        for gid in tqdm(selected_idx, desc='Query'):
-            text = ptb[gid]['text']
-
-            for eid, ent in enumerate(ents):
-                # if os.path.exists(f'result/openai_result/{args.model}/ptb/per_ent/{ent}/{gid}.pkl') or \
-                # os.path.exists(f'result/openai_result/{args.model}/ptb/per_ent/{ent}/{gid}.txt'):
-                #     print(gid, ent, 'skip')
-                #     continue
-
-                ## Get prompt
-                msg = template_single.format(ents_prompt[eid], text)
-
-                if 'gpt' in args.model:
-                    prompt = msg  
-
-                elif 'vicuna' in args.model or 'alpaca' in args.model or 'fastchat-t5' in args.model:
-                    conv = get_conversation_template(args.model)
-                    conv.append_message(conv.roles[0], msg)
-                    conv.append_message(conv.roles[1], None)
-                    conv.system = ''
-                    prompt = conv.get_prompt().strip()
-
-                elif 'llama-' in args.model:
-                    prompt = '### Human: ' + msg + ' ### Assistant:'
-
-
-                ## Run
-                if 'gpt3' in args.model:
-                    outputs = gpt3(prompt)
-                    
-                else:
-                    outputs = fastchat(prompt, model, tokenizer)
-
-                with open(f'result/openai_result/{args.model}/ptb/per_ent/{ent}/{gid}.txt', 'w') as f:
-                    f.write(outputs)
-
-
-    if args.prompt == 2:
-        for gid in tqdm(selected_idx, desc='Query'):
-            text = ptb[gid]['text']
-
-            if os.path.exists(f'result/structured_prompt/{args.model}/ptb/{gid}.pkl') or \
-            os.path.exists(f'result/structured_prompt/{args.model}/ptb/{gid}.txt'):
-                print(gid, 'skip')
-                continue
-
-            prompt = demonstration + '\n' + text
-
-            if 'gpt3' in args.model:
-                outputs = gpt3(prompt)
-
-            else:
-                outputs = fastchat(prompt, model, tokenizer)
-
-            with open(f'result/structured_prompt/{args.model}/ptb/{gid}.txt', 'w') as f:
-                f.write(outputs)
-
-
-def fastchat(prompt, model, tokenizer):
-    input_ids = tokenizer([prompt]).input_ids
-    output_ids = model.generate(
-        torch.as_tensor(input_ids).cuda(),
-        do_sample=True,
-        temperature=args.temperature,
-        repetition_penalty=args.repetition_penalty,
-        max_new_tokens=args.max_new_tokens,
-    )
-
-    if model.config.is_encoder_decoder:
-        output_ids = output_ids[0]
-    else:
-        output_ids = output_ids[0][len(input_ids[0]) :]
-    outputs = tokenizer.decode(
-        output_ids, skip_special_tokens=True, spaces_between_special_tokens=False
-    )
-
-    # print('Empty system message')
-    # print(f"{conv.roles[0]}: {msg}")
-    # print(f"{conv.roles[1]}: {outputs}")
-
-    return outputs
-
-
-def gpt3(prompt):
-    try:
-        response = openai.ChatCompletion.create(
-            model=args.model, messages=[{"role": "user", "content": prompt}])
-
-        return response
-
-    except Exception as err:
-        print('Error')
-        print(err)
-
-    # time.sleep(1)
-    raise
-
+import subprocess
+from gradio.mix import Parallel
+
+def qa_prompting(model):
+    # Call your `run_llm.py` script for QA-Based Prompting with the selected model
+    output = subprocess.check_output([sys.executable, "run_llm.py", "--model", model, ...], text=True)
+    return output
+
+def strategy_1_interface():
+    model_names = ["ChatGPT", "LLaMA", "Vicuna", "Alpaca", "Flan-T5"]
+    interfaces = []
+    for model_name in model_names:
+        interfaces.append(gr.Interface(
+            fn=qa_prompting,
+            inputs=gr.inputs.Textbox(label=f"{model_name} Input"),
+            outputs=gr.outputs.Textbox(label=f"{model_name} Output"),
+            title=f"Strategy 1 - QA-Based Prompting: {model_name}",
+        ))
+
+    return Parallel(*interfaces)
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    add_model_args(parser)
-    parser.add_argument("--temperature", type=float, default=0.7)
-    parser.add_argument("--repetition_penalty", type=float, default=1.0)
-    parser.add_argument("--max-new-tokens", type=int, default=512)
-    parser.add_argument("--debug", action="store_true")
-    parser.add_argument("--message", type=str, default="Hello! Who are you?")
-    parser.add_argument("--start", type=int, default=0)
-    parser.add_argument("--end", type=int, default=1)
-    parser.add_argument("--model", required=True, type=str, default=None)
-    parser.add_argument("--prompt", required=True, type=int, default=None)
-    args = parser.parse_args()
-
-    # Reset default repetition penalty for T5 models.
-    if "t5" in args.model and args.repetition_penalty == 1.0:
-        args.repetition_penalty = 1.2
-
-    main(args)
-
-
+    iface = strategy_1_interface()
+    iface.launch()