add application files

app.py

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+import gradio as gr
+from utils import *
+import openai
+
+def greet(task, human_input, cooperate_style):
+    merged_answer = "xxx"
+    return f"Given task :{task},\nthe {cooperate_style} answer based of ai and human input \"{human_input}\" is:\n {merged_answer}. "
+
+def get_answer(task, human_input, cooperate_style):
+    if cooperate_style=="sequential":
+            merged_text = merge_texts_sequential(task, human_input)
+            print(f"AI merged text: {merged_text}")
+    else:
+        generated_text = generate_text_with_gpt(task)
+        print(f"AI provided text: {generated_text}")    
+        merged_text = merge_texts_parallel(task, human_input, generated_text)
+    return merged_text
+
+def main():
+    with gr.Blocks() as demo:
+        task = gr.Textbox(label="Please Give a task description.")
+        human_input = gr.Textbox(label="Please provide a human answer.", value = "Write a poem about the moon.")
+        cooperate_style = gr.Radio(['sequential', 'parallel'], label="What type of cooperation would you like?")
+        greet_btn = gr.Button("Think!")
+        output = gr.Textbox(label="Answer with cooperation")
+        
+        greet_btn.click(fn=get_answer, 
+                        inputs=[task, human_input, cooperate_style], 
+                        outputs=output, 
+                        api_name="answer")
+        
+        evaluate_btn = gr.Button("Evaluate")
+        evaluation_results = gr.Textbox(label="Evaluation results.")
+        evaluate_btn.click(fn= evaluation,
+                           inputs=[task, output],
+                           outputs= evaluation_results,
+                           api_name = "evaluate")
+        demo.launch(share=True)
+    
+if __name__ == "__main__":
+    main()
+    
+
+

configs.py

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+OPEN_API_KEY = "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"

requirements.txt

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+openai
+gradio

utils.py

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+import openai 
+from configs import OPEN_API_KEY
+def get_user_input(prompt):
+    return input(prompt)
+
+def choose_cooperation_type():
+    choice = get_user_input("Please choose the cooperation type: \n" 
+                            +"1. Sequential: human provide an answer first and then AI provide the answer based on it.\n" 
+                            +"2. Parallel: human and AI give answers seperately and then AI does the merge.\n")
+    if choice == '1':
+        return 'sequential'
+    elif choice == '2':
+        return 'parallel'
+    else:
+        print("Invalid choice. Please try again.")
+        return choose_cooperation_type()
+
+def describe_task():
+    task_description = get_user_input("Please describe your task: ")
+    if task_description.strip() == "":
+        # print("Task description cannot be empty. Please try again.")
+        # return describe_task()
+        task_description = "Write a poem about the moon in 3 lines."
+    return task_description
+
+# def get_api_key():
+#     return configs.api_key  # get the API key from the configs file
+
+def generate_text_with_gpt(promts):
+    client = openai.OpenAI(api_key=OPEN_API_KEY)
+    
+    try:
+        response = client.chat.completions.create(
+        model="gpt-3.5-turbo",
+        messages=[
+            {"role": "system", "content": promts},
+            ]
+        )
+        return response.choices[0].message.content
+    except Exception as e:
+        print(f"Error occured when generating texts: {e}")
+        return ""
+
+def merge_texts_parallel(task_description, human_text, ai_text):
+    prompt = f"Given the task as :{task_description}, there are two answers provided:\n" + \
+             f"The first answer:  {human_text}\nThe second answer: {ai_text}\n" + \
+             f"Merge the two answers into one in a coherent way: "
+    return generate_text_with_gpt(prompt)
+
+def merge_texts_sequential(task_description, human_text):
+    prompt = f"Given the task as :{task_description}, the human answer is: {human_text}\n" + \
+             f"Provide an answer of your own but make sure it is coherent and should be based on the human answer: "
+    return generate_text_with_gpt(prompt)
+
+def evaluation(task_description, text):
+    prompt = f"Given the task as :{task_description}, the answer provided is: {text}\n" + \
+             f"Evaluate the answer and provide scores between 0 and 10,\n" + \
+             f"where criteria for evaluation are correctness, relevance, novelty, fluency, and aesthetic Value:"
+    return generate_text_with_gpt(prompt)