Create app.py

app.py

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+from crewai import Agent, Task, Crew, Process
+from langchain_google_genai import ChatGoogleGenerativeAI
+from crewai_tools import SerperDevTool
+import os
+from langchain_community.utilities import GoogleSerperAPIWrapper
+from langchain_community.tools import DuckDuckGoSearchRun
+from langchain_openai import OpenAI
+from langchain.agents import initialize_agent, Tool
+from crewai_tools import tool
+import gradio as gr
+
+
+@tool('DuckDuckGoSearch')
+def search(search_query: str):
+    """Search the web for information on a given topic"""
+    return DuckDuckGoSearchRun().run(search_query)
+
+
+
+llm = ChatGoogleGenerativeAI(
+        model="gemini-pro", verbose=True, temperature=0.1, google_api_key=os.getenv('GOOGLE_API_KEY')
+    )
+
+search = DuckDuckGoSearchRun()
+@tool('DuckDuckGoSearch')
+def search(search_query: str):
+    """Search the web for information on a given topic"""
+    return DuckDuckGoSearchRun(max_results=5).run(search_query)
+
+SerpBot = Agent(
+  role='Search Expert',
+  goal=' You find all the information that the Writer requests you to find. Then you compile it and give it back to him.',
+  backstory="""
+  You are an agent that uses SERP in the best way to search for the input query. Action input
+  is the following format: {"search_query"="input text"}
+  """,
+  verbose=True,
+  tools=[search],
+  llm=llm
+)
+
+Writer = Agent(
+      role='Expert Writer',
+      goal='Write engaging, well written articles with a pinch of humour',
+      backstory="""
+      A seasoned funny writer who writes about the latest news on that topic and 
+      can convert boring information to amazing articles that everyone loves reading.
+      Your articles have a satirical tone and the humour is based on real life events
+      """,
+      verbose=True,
+      allow_delegation=True,
+      tools=[search],
+      llm=llm
+)
+
+def get_human_input(input):
+    """Get the human input for the search query"""
+    human_input = input
+    return human_input
+
+def create_task(human_input):
+    """Create a task based on the human input"""
+    task1 = Task(
+        description=f"Write a newsletter based on the following topics: +{human_input}. Research on topic separately and write detailed articles about them",
+        agent = Writer,
+        human_feedback=True,
+        expected_output="When the entire newsletter is generated",
+    )
+    return task1
+
+def create_crew(task):
+    """Create a crew with the task and agents"""
+    story_crew = Crew(
+        agents=[SerpBot, Writer],
+        tasks=[task],
+        verbose=True,
+        process=Process.sequential,
+    )
+    return story_crew
+
+def kickoff_crew(story_crew):
+    """Kick off the crew"""
+    story_output = story_crew.kickoff()
+    return story_output
+
+def response(input,history=[]):
+    human_input = get_human_input(input)
+    task = create_task(human_input)
+    story_crew = create_crew(task)
+    res = kickoff_crew(story_crew)
+    return res
+
+
+#story_output = kickoff_crew(story_crew)
+gr.ChatInterface(response).launch(share=True, debug=True)
+
+