Update app.py

app.py

@@ -4,7 +4,90 @@
 
 from langchain_community.llms import HuggingFaceHub
 
+#from langchain_community.llms import HuggingFaceHub
+
+llm_zephyr-7b-beta = HuggingFaceHub(
+    repo_id="HuggingFaceH4/zephyr-7b-beta",
+    task="text-generation",
+    model_kwargs={
+        "max_new_tokens": 512,
+        "top_k": 30,
+        "temperature": 0.1,
+        "repetition_penalty": 1.03,
+    },
+)
+
+import os
+from crewai import Agent, Task, Crew, Process
+from crewai_tools import SerperDevTool
+
+search_tool = SerperDevTool()
+
+# Define your agents with roles and goals
+researcher = Agent(
+  role='Senior Research Analyst',
+  goal='Uncover cutting-edge developments in AI and data science',
+  backstory="""You work at a leading tech think tank.
+  Your expertise lies in identifying emerging trends.
+  You have a knack for dissecting complex data and presenting actionable insights.""",
+  verbose=True,
+  allow_delegation=False,
+  tools=[search_tool],
+  llm=llm_zephyr-7b-beta
+  # You can pass an optional llm attribute specifying what mode you wanna use.
+  # It can be a local model through Ollama / LM Studio or a remote
+  # model like OpenAI, Mistral, Antrophic or others (https://docs.crewai.com/how-to/LLM-Connections/)
+  #
+  # import os
+  # os.environ['OPENAI_MODEL_NAME'] = 'gpt-3.5-turbo'
+  #
+  # OR
+  #
+  # from langchain_openai import ChatOpenAI
+  # llm=ChatOpenAI(model_name="gpt-3.5", temperature=0.7)
+)
+
+writer = Agent(
+  role='Tech Content Strategist',
+  goal='Craft compelling content on tech advancements',
+  backstory="""You are a renowned Content Strategist, known for your insightful and engaging articles.
+  You transform complex concepts into compelling narratives.""",
+  verbose=True,
+  allow_delegation=True],
+  llm=llm_zephyr-7b-beta
+)
+
+# Create tasks for your agents
+task1 = Task(
+  description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
+  Identify key trends, breakthrough technologies, and potential industry impacts.""",
+  expected_output="Full analysis report in bullet points",
+  agent=researcher
+)
 
+task2 = Task(
+  description="""Using the insights provided, develop an engaging blog
+  post that highlights the most significant AI advancements.
+  Your post should be informative yet accessible, catering to a tech-savvy audience.
+  Make it sound cool, avoid complex words so it doesn't sound like AI.""",
+  expected_output="Full blog post of at least 4 paragraphs",
+  agent=writer
+)
+
+# Instantiate your crew with a sequential process
+crew = Crew(
+  agents=[researcher, writer],
+  tasks=[task1, task2],
+  verbose=2, # You can set it to 1 or 2 to different logging levels
+)
+
+# Get your crew to work!
+#result = crew.kickoff()
+
+#print("######################")
+#print(result)
+
+##################
 ###### other models:
 # "Trelis/Llama-2-7b-chat-hf-sharded-bf16"
 # "bn22/Mistral-7B-Instruct-v0.1-sharded"
@@ -288,12 +371,17 @@ fe_app = gr.ChatInterface(
 # load the model asynchronously on startup and save it into memory 
 @app.on_event("startup")
 async def startup():
-    domain_url = 'https://globl.contact/'
-    links = get_all_links_from_domain(domain_url)
-    print("Links from the domain:", links)
+    # Get your crew to work!
+    result = crew.kickoff()
+
+    print("######################")
+    print(result)
+    #domain_url = 'https://globl.contact/'
+    #links = get_all_links_from_domain(domain_url)
+    #print("Links from the domain:", links)
     
     #########
     # Assuming visited_urls is a list of URLs
-    for url in links:
-        vs = get_vectorstore_from_url(url)
+    #for url in links:
+    #    vs = get_vectorstore_from_url(url)
     #load_model()