Create app.py

app.py

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+import os
+from knowledge_storm import STORMWikiRunnerArguments, STORMWikiRunner, STORMWikiLMConfigs
+from knowledge_storm.lm import OpenAIModel
+from knowledge_storm.rm import YouRM
+
+lm_configs = STORMWikiLMConfigs()
+openai_kwargs = {
+    'api_key': os.getenv("OPENAI_API_KEY"),
+    'temperature': 1.0,
+    'top_p': 0.9,
+}
+# STORM is a LM system so different components can be powered by different models to reach a good balance between cost and quality.
+# For a good practice, choose a cheaper/faster model for `conv_simulator_lm` which is used to split queries, synthesize answers in the conversation.
+# Choose a more powerful model for `article_gen_lm` to generate verifiable text with citations.
+gpt_35 = OpenAIModel(model='gpt-3.5-turbo', max_tokens=500, **openai_kwargs)
+gpt_4 = OpenAIModel(model='gpt-4o', max_tokens=3000, **openai_kwargs)
+lm_configs.set_conv_simulator_lm(gpt_4)
+lm_configs.set_question_asker_lm(gpt_4)
+lm_configs.set_outline_gen_lm(gpt_4)
+lm_configs.set_article_gen_lm(gpt_4)
+lm_configs.set_article_polish_lm(gpt_4)
+
+# Check out the STORMWikiRunnerArguments class for more configurations.
+engine_args = STORMWikiRunnerArguments("outputs")
+rm = YouRM(ydc_api_key=os.getenv('YDC_API_KEY'), k=engine_args.search_top_k)
+runner = STORMWikiRunner(engine_args, lm_configs, rm)
+
+def generate_article(prompt, progress=gr.Progress(track_tqdm=True)):
+    runner.run(
+        topic=prompt,
+        do_research=True,
+        do_generate_outline=True,
+        do_generate_article=True,
+        do_polish_article=True,
+    )
+    runner.post_run()
+    runner.summary()
+
+