Create app.py

app.py

@@ -0,0 +1,156 @@
+import gradio as gr
+import openai
+import requests
+import json
+import os
+from duckduckgo_search import ddg
+
+os.environ["OPENAI_API_KEY"] = "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"
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+def search_duckduckgo(query):
+
+    keywords = query
+    results = ddg(keywords, region='wt-wt', safesearch='Off', time='m')
+    filtered_results = [{"title": res["title"], "body": res["body"]} for res in results]
+    print(filtered_results)
+    return filtered_results
+
+def get_search_query(task):
+    
+    response = openai.ChatCompletion.create(
+      model="gpt-3.5-turbo",
+      messages=[
+        {"role": "user", "content": f"Given the task: {task}. Generate a concise search query with 1-3 keywords."}
+      ]
+    )
+    search_query = response.choices[0]['message']['content'].strip()
+    print("Agent 2: ",search_query)
+    return search_query
+
+def summarize_search_result(task, search_result):
+    
+    response = openai.ChatCompletion.create(
+      model="gpt-3.5-turbo",
+      messages=[
+        {"role": "user", "content":f"Given the task '{task}' and the search result '{json.dumps(search_result)}', provide a summarized result."}
+      ]
+    )
+    summary = response.choices[0]['message']['content'].strip()
+    return summary
+
+def agent_1(objective):
+    
+    response = openai.ChatCompletion.create(
+      model="gpt-3.5-turbo",
+      messages=[
+        {"role": "user", "content":f"Given the objective '{objective}', create a list of tasks that are closely related to the objective. If needed add specific key words from objective to the task sentence"}
+      ]
+    )
+    tasks = response.choices[0]['message']['content'].strip().split('\n')
+    return tasks
+
+def agent_2(task):
+    
+    search_query = get_search_query(task)
+    print("Agent 2")
+    print(search_query)
+    search_results = search_duckduckgo(search_query)
+    summarized_result = summarize_search_result(task, search_results)
+    print(summarized_result)
+    return summarized_result
+
+def agent_3(objective, last_result, tasks):
+    
+    task_list = '\n'.join(tasks)
+    response = openai.ChatCompletion.create(
+      model="gpt-3.5-turbo",
+      messages=[
+        {"role": "user", "content":f"Given the objective '{objective}', the last result '{json.dumps(last_result)}', and the task list:\n{task_list}\n\nRank the tasks based on their relevance to the objective."}
+      ]
+    )
+    modified_tasks = response.choices[0]['message']['content'].strip().split('\n')
+    print("Agent 3")
+    print(modified_tasks)
+    return modified_tasks
+
+def summarize_result(objective, result):
+    
+    response = openai.ChatCompletion.create(
+      model="gpt-3.5-turbo",
+      messages=[
+        {"role": "user", "content":f"Given the objective '{objective}' and the final result '{json.dumps(result)}', provide a summary."}
+      ]
+    )
+    summary = response.choices[0]['message']['content'].strip()
+    return summary
+
+def generate_final_answer(objective, all_results):
+
+    response = openai.ChatCompletion.create(
+      model="gpt-3.5-turbo",
+      messages=[
+        {"role": "user", "content":f"Given the objective '{objective}' and the collected results '{json.dumps(all_results)}', provide a final answer addressing the objective."}
+      ]
+    )
+    final_answer = response.choices[0]['message']['content'].strip()
+    return final_answer
+
+def main(objective, loop_count):
+    tasks = agent_1(objective)
+    all_results = []
+    completed_tasks = []
+    for i in range(loop_count):
+        print(i+1)
+        if i < len(tasks):
+            print('NEXT TASK: ',tasks[i])
+            completed_tasks.append(tasks[i])
+            result = agent_2(tasks[i])
+            all_results.append(result)
+            tasks = agent_3(objective, result, tasks)
+            print('*********************')
+        else:
+            break
+
+    final_answer = generate_final_answer(objective, all_results)
+    return final_answer,completed_tasks,tasks
+
+def getbabyagianswer(objective,loop_count,openapikey):
+    objective=objective
+
+    loop_count = loop_count
+    final_summary,completed_tasts,all_tasks = main(objective, loop_count)
+    print("Final Summary:", final_summary)
+    return final_summary,completed_tasts,all_tasks
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("<h1><center>GPT4 created BabyAGI</center></h1>")
+    gr.Markdown(
+        """ This is part of a series of experiments using BabyAGI as a "framework" to construct focused use cases (ex: idea generation). In this GPT-4 was prompted to create a BabyAGI with task creation & execution agents but with constraints to give answer with a specified number of loops. Unlike the original BabyAGI concept, this is not open-ended. \n\nNote: This is a series of experiments to understand AI agents and hence do check the quality of output. OpenAI agents (gpt-3.5-turbo) & DuckDuckGo search are used. The analysis takes roughly 120 secs & may not always be consistent. An error occurs when the OpenAI Api key is not provided/ ChatGPT API is overloaded/ ChatGPT is unable to correctly decipher & format the output\n\n[visitors](https://visitor-badge.glitch.me/badge?page_id=hra/GPT4-makes-BabyAGI)"""
+        )
+    
+    with gr.Row() as row:
+        with gr.Column():
+            textboxtopic = gr.Textbox(placeholder="Enter Topic for Curriculum...", lines=1,label='Topic')
+        with gr.Column():
+            textboxtopic = gr.Textbox(placeholder="Enter # of loops...", lines=1,label='Loop Count')
+        with gr.Column():
+            textboxopenapi = gr.Textbox(placeholder="Enter OpenAPI Key...", lines=1,label='OpenAPI Key')
+    with gr.Row() as row:
+        examples = gr.Examples(examples=['Give me a startup idea in healthcare technology for India','Which is a must see destination in Mysore?','Find me a unique cuisine restaurant in bangalore','Give me a startup idea for AI in music streaming'],
+                           inputs=[textboxtopic])
+    with gr.Row() as row:
+        btn = gr.Button("Unleash AI Agent")
+        
+    with gr.Row() as row:
+        with gr.Column():
+            answer1 = gr.Textbox(placeholder="", lines=1,label='Answer')
+        with gr.Column():
+            fulltasklist1 = gr.Textbox(placeholder="", lines=1,label='Full Task List')
+        with gr.Column():
+            completedtasklist1 = gr.Textbox(placeholder="", lines=1,label='Completed Tasks')
+    
+    btn.click(getbabyagianswer, inputs=[textboxtopic,textboxloopcount,textboxopenapi,],outputs=[answer1,fulltasklist1,completedtasklist1])
+    
+demo.launch(debug=True)