Update app.py

app.py

@@ -1,3 +1,12 @@
+import os
+import gradio as gr
+from langchain.chat_models import ChatOpenAI
+from langchain import LLMChain, PromptTemplate
+from langchain.memory import ConversationBufferMemory
+%pip install langchain_google_genai load_dotenv crewai crewai_tools langchain_community langchain sentence-transformers langchain-groq langchain_huggingface --quiet openai gradio huggingface_hub
+
+
+
 import os
 import gradio as gr
 from langchain.chat_models import ChatOpenAI
@@ -6,10 +15,124 @@ from langchain.memory import ConversationBufferMemory
 
 OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
 
-template = """You are a helpful assistant to answer all user queries.
-{chat_history}
-User: {user_message}
-Chatbot:"""
+from crewai import Agent, Task, Crew
+
+from google.colab import userdata
+from dotenv import load_dotenv
+from langchain_openai import ChatOpenAI
+import os
+load_dotenv()
+
+from crewai import Agent, Task, Crew
+from crewai import LLM
+os.environ['GEMINI_API_KEY'] = userdata.get('Gemini_Api')
+llm = LLM(model="gemini/gemini-1.5-flash")
+# os.environ['GEMINI_API_KEY'] = userdata.get('GEMINI_API_KEY')
+os.environ['GROQ_API_KEY']=userdata.get('GROQ_API')
+
+
+import crewai_tools
+from crewai import tools
+
+
+from crewai_tools import SerperDevTool
+tool = SerperDevTool()
+
+
+scrapper_agent = Agent(
+    role="Senior Scrapper Representative",
+	goal="Be the most friendly and helpful "
+        "Scrapper representative in your team to scrape information inputted by user of query {query} ",
+	backstory=(
+		"You have scrapped many information inputted by user of query {query} and "
+        "you are good and perfect at it and makes this task easy "
+		"You need to make sure that you provide the best support!"
+		"Make sure to provide full complete answers, "
+        " and make no assumptions."
+	),
+	allow_delegation=False,
+	llm=llm,
+	verbose=True
+)
+Provider_agent = Agent(
+    role="Senior information Provider Representative",
+	goal="Be the most friendly and helpful "
+        "information provider in your team to provide the information scrapped from web browser",
+	backstory=(
+		"You have provided many information that were scrapped by other agent from web browser and "
+        "you are good and perfect at it and makes this task easy "
+		"You need to make sure that you provide the best support!"
+		"Make sure to provide full complete answers, "
+        " and make no assumptions."
+	),
+	allow_delegation=False,
+	llm=llm,
+	verbose=True
+)
+
+scrapper_task = Task(
+    description=(
+        "user just reached out with a super important task"
+	    "to scrape information from web browser of query {query} "
+		"Make sure to use everything you know "
+        "to provide the best support possible."
+		"You must strive to provide a complete "
+        "and accurate response to the user's query."
+    ),
+    expected_output=(
+	    "A detailed, informative response to the "
+        "user's query that addresses "
+        "all aspects of their question.\n"
+        "The response should include references "
+        "to everything you used to find the answer, "
+        "including external data or solutions. "
+        "Ensure the answer is complete, "
+		"leaving no questions unanswered, and maintain a helpful and friendly "
+		"tone throughout."
+    ),
+	tools=[tool],
+    agent=scrapper_agent,
+)
+Provider_task = Task(
+    description=(
+        "Your task is to make proper documented information that are scrapped from other agent "
+		"Make sure to use everything you know "
+        "to provide the best support possible."
+		"You must strive to provide a complete "
+        "and accurate response to the user's query."
+    ),
+    expected_output=(
+	    "A detailed, informative response to the "
+        "user's query that addresses and make it well and perfect dcumented to easily readable "
+        "all aspects of their question.\n"
+        "The response should include references "
+        "to everything you used to find the answer, "
+        "including external data or solutions. "
+        "Ensure the answer is complete, "
+		"leaving no questions unanswered, and maintain a helpful and friendly "
+		"tone throughout."
+    ),
+    agent=Provider_agent,
+)
+
+crew = Crew(
+  agents=[scrapper_agent, Provider_agent],
+  tasks=[scrapper_task, Provider_task],
+  verbose=True
+)
+
+# inputs = {
+#     "query": input("Enter your query: "),
+#     # "url": input("Enter which source to use for query: ")
+# }
+# result = crew.kickoff(inputs=inputs)
+def get_text_response(message, history):
+    result = crew.kickoff(inputs={"query": message})
+    return result.raw
+# from IPython.display import Markdown
+# Markdown(result.raw)
+
+# demo = gr.ChatInterface(get_text_response, examples=["How are you doing?","What are your interests?","Which places do you like to visit?"])
 
 prompt = PromptTemplate(
     input_variables=["chat_history", "user_message"], template=template
@@ -24,9 +147,6 @@ llm_chain = LLMChain(
     memory=memory,
 )
 
-def get_text_response(user_message,history):
-    response = llm_chain.predict(user_message = user_message)
-    return response
 
 demo = gr.ChatInterface(get_text_response)