Update app.py

app.py

@@ -1,234 +1,96 @@
 import os
+from crewai import Agent, Task, Crew, Process
+from langchain.tools import DuckDuckGoSearchRun
+from langchain.agents import Tool
 import gradio as gr
-from llama_index.llms.base import ChatMessage
-from llama_index.llms import Perplexity
-from openai import OpenAI
 
-
-MODEL_CHOICES_LLM1 = [
-    "pplx-7b-online", "pplx-70b-online", "codellama-34b-instruct", "llama-2-70b-chat", "mistral-7b-instruct",
-    "mixtral-8x7b-instruct", "pplx-7b-chat", "pplx-70b-chat"  # Add actual model names here
-]
-
-MODEL_CHOICES_LLM2 = [
-    "gpt-4", "gpt-4-1106-preview", "gpt-3.5-turbo", "gpt-3.5-turbo-1106", " gpt-3.5-turbo-0613", "dall-e-3"   # Add actual model names here
-]
-
-# Initialize default variables and models for both LLMs
-DEFAULT_LLM1_MODEL = "pplx-70b-online"
-DEFAULT_LLM2_MODEL = "gpt-4"
-SYSTEM_MESSAGE_1, PREP_PROMPT_1, AFTER_PROMPT_1, LLM1_MODEL = "", "", "", DEFAULT_LLM1_MODEL
-SYSTEM_MESSAGE_2, PREP_PROMPT_2, LLM2_MODEL = "", "", DEFAULT_LLM2_MODEL
-
-# Initialize Perplexity model (LLM 1)
-pplx_api_key = os.getenv('PPX_API_KEY')
-llm1 = Perplexity(api_key=pplx_api_key, model=LLM1_MODEL, temperature=0.5)
-
-# Initialize OpenAI GPT model (LLM 2)
-openai_api_key = os.getenv('OPENAI_API_KEY')
-client = OpenAI(api_key=openai_api_key)
-OpenAItemp = 1.0
-
-## LLM Chain Functions ##
-def update_variables_and_model_llm1(model_name, system_message_1, prep_prompt_1, after_prompt_1):
-    global SYSTEM_MESSAGE_1, PREP_PROMPT_1, AFTER_PROMPT_1, LLM1_MODEL, llm1
-    SYSTEM_MESSAGE_1, PREP_PROMPT_1, AFTER_PROMPT_1 = system_message_1.strip(), prep_prompt_1.strip(), after_prompt_1.strip()
-    LLM1_MODEL = model_name.strip()
-    llm1 = Perplexity(api_key=pplx_api_key, model=LLM1_MODEL, temperature=0.5)
-    return "Variables and model updated for LLM 1!"
-
-def update_variables_and_model_llm2(model_name, system_message_2, prep_prompt_2):
-    global SYSTEM_MESSAGE_2, PREP_PROMPT_2, LLM2_MODEL
-    SYSTEM_MESSAGE_2, PREP_PROMPT_2 = system_message_2.strip(), prep_prompt_2.strip()
-    LLM2_MODEL = model_name.strip()
-    return "Variables and model updated for LLM 2!"
-
-def reset_variables_llm1():
-    global SYSTEM_MESSAGE_1, PREP_PROMPT_1, AFTER_PROMPT_1, LLM1_MODEL, llm1
-    SYSTEM_MESSAGE_1, PREP_PROMPT_1, AFTER_PROMPT_1 = "", "", ""
-    LLM1_MODEL = DEFAULT_LLM1_MODEL
-    llm1 = Perplexity(api_key=pplx_api_key, model=LLM1_MODEL, temperature=0.5)
-    return "Variables and model reset for LLM 1!"
-
-def reset_variables_llm2():
-    global SYSTEM_MESSAGE_2, PREP_PROMPT_2, LLM2_MODEL
-    SYSTEM_MESSAGE_2, PREP_PROMPT_2 = "", ""
-    LLM2_MODEL = DEFAULT_LLM2_MODEL
-    return "Variables and model reset for LLM 2!"
-
-
-## FINAL OUTPUT with only send pre,after,system if filled in and not an empty space##
-def chat_with_llms(message):
-    if not message.strip():
-        return "No input provided. Please enter a message."
-
-    # First LLM Chain
-    messages_1 = []
-    if SYSTEM_MESSAGE_1:  # Add system message only if it's not empty
-        messages_1.append(ChatMessage(role="system", content=SYSTEM_MESSAGE_1))
-    # Prepare the user message for LLM 1
-    user_message_1 = ""
-    if PREP_PROMPT_1:
-        user_message_1 += PREP_PROMPT_1
-    user_message_1 += message
-    if AFTER_PROMPT_1:
-        user_message_1 += AFTER_PROMPT_1
-    messages_1.append(ChatMessage(role="user", content=user_message_1))
-    response_1 = llm1.chat(messages_1).message.content
-
-    # Second LLM Chain
-    messages_2 = []
-    if SYSTEM_MESSAGE_2:  # Add system message only if it's not empty
-        messages_2.append({"role": "system", "content": SYSTEM_MESSAGE_2})
-    # Prepare the user message for LLM 2
-    user_message_2 = ""
-    if PREP_PROMPT_2:
-        user_message_2 += PREP_PROMPT_2
-    user_message_2 += response_1
-    messages_2.append({"role": "user", "content": user_message_2})
-    completion = client.chat.completions.create(model=LLM2_MODEL, temperature=OpenAItemp, messages=messages_2)
-    response_2 = completion.choices[0].message.content
-
-    return response_2  # Return only the response from LLM 2
-
-
-### FINAL CONTENT + DO NOT SEND EMPTY VALUES ###
-'''
-def chat_with_llms(message):
-    if not message.strip():
-        return "No input provided. Please enter a message."
-
-    # First LLM Chain
-    messages_1 = []
-    if SYSTEM_MESSAGE_1:  # Add system message only if it's not empty
-        messages_1.append(ChatMessage(role="system", content=SYSTEM_MESSAGE_1))
-    messages_1.append(ChatMessage(role="user", content=(PREP_PROMPT_1 if PREP_PROMPT_1 else "") + message + (AFTER_PROMPT_1 if AFTER_PROMPT_1 else "")))
-    response_1 = llm1.chat(messages_1).message.content
-
-    # Second LLM Chain
-    messages_2 = []
-    if SYSTEM_MESSAGE_2:  # Add system message only if it's not empty
-        messages_2.append({"role": "system", "content": SYSTEM_MESSAGE_2})
-    messages_2.append({"role": "user", "content": (PREP_PROMPT_2 if PREP_PROMPT_2 else "") + response_1})
-    completion = client.chat.completions.create(model="gpt-4", temperature=OpenAItemp, messages=messages_2)
-    response_2 = completion.choices[0].message.content
-
-    return response_2  # Return only the response from LLM 2
-
-##SHOW FULL INPUTS and OUTPUTS FOR LLM1 AND 2 and exclude any empty values from inputs##
-def chat_with_llms(message):
-    if not message.strip():
-        return "No input provided. Please enter a message."
-
-    # First LLM Chain
-    messages_1 = []
-    if SYSTEM_MESSAGE_1:  # Add system message only if it's not empty
-        messages_1.append(ChatMessage(role="system", content=SYSTEM_MESSAGE_1))
-    messages_1.append(ChatMessage(role="user", content=(PREP_PROMPT_1 if PREP_PROMPT_1 else "") + message + (AFTER_PROMPT_1 if AFTER_PROMPT_1 else "")))
-    response_1 = llm1.chat(messages_1).message.content
-
-    # Full message chain for LLM 1
-    full_message_chain_llm1 = 'LLM 1 Conversation:\n'
-    for msg in messages_1:
-        full_message_chain_llm1 += f"{msg.role.title()}: {msg.content}\n"
-    full_message_chain_llm1 += f"Assistant: {response_1}\n\n"
-
-    # Second LLM Chain
-    messages_2 = []
-    if SYSTEM_MESSAGE_2:  # Add system message only if it's not empty
-        messages_2.append({"role": "system", "content": SYSTEM_MESSAGE_2})
-    messages_2.append({"role": "user", "content": (PREP_PROMPT_2 if PREP_PROMPT_2 else "") + response_1})
-    completion = client.chat.completions.create(model="gpt-4", temperature=OpenAItemp, messages=messages_2)
-    response_2 = completion.choices[0].message.content
-
-    # Full message chain for LLM 2
-    full_message_chain_llm2 = 'LLM 2 Conversation:\n'
-    for msg in messages_2:
-        full_message_chain_llm2 += f"{msg['role'].title()}: {msg['content']}\n"
-    full_message_chain_llm2 += f"Assistant: {response_2}\n"
-
-    return full_message_chain_llm1 + full_message_chain_llm2
-
-### SHOW FULL LLMS INPUTS ##
-def chat_with_llms(message):
-    # Check if the input message is empty or only contains whitespace
-    if not message.strip():
-        return "No input provided. Please enter a message."
-
-    # First LLM Chain
-    messages_1 = [ChatMessage(role="system", content=SYSTEM_MESSAGE_1), ChatMessage(role="user", content=PREP_PROMPT_1 + message + AFTER_PROMPT_1)]
-    response_1 = llm1.chat(messages_1).message.content
-
-    # Build the full message chain for LLM 1
-    full_message_chain_llm1 = 'LLM 1 Conversation:\n'
-    for msg in messages_1:
-        full_message_chain_llm1 += f"{msg.role.title()}: {msg.content}\n"
-    full_message_chain_llm1 += f"Assistant: {response_1}\n\n"
-
-    # Second LLM Chain
-    messages_2 = [
-        {"role": "system", "content": SYSTEM_MESSAGE_2},
-        {"role": "user", "content": PREP_PROMPT_2 + response_1}
-    ]
-    completion = client.chat.completions.create(model="gpt-4", temperature=OpenAItemp, messages=messages_2)
-    response_2 = completion.choices[0].message.content
-
-    # Build the full message chain for LLM 2
-    full_message_chain_llm2 = 'LLM 2 Conversation:\n'
-    for msg in messages_2:
-        full_message_chain_llm2 += f"{msg['role'].title()}: {msg['content']}\n"
-    full_message_chain_llm2 += f"Assistant: {response_2}\n"
-
-    # Return the full conversation histories of both LLMs
-    return full_message_chain_llm1 + full_message_chain_llm2
-
-'''
-
-# Gradio interface for updating LLM 1 variables and model
-llm1_interface = gr.Interface(
-    fn=update_variables_and_model_llm1,
-    inputs=[
-        gr.Dropdown(choices=MODEL_CHOICES_LLM1, label="LLM 1 - PPX - Models", value=DEFAULT_LLM1_MODEL),  # Dropdown for LLM1
-        "text", "text", "text"
-    ],
-    outputs="text",
-    title="Update Variables and Model for LLM 1"
-)
-reset_llm1_interface = gr.Interface(
-    fn=reset_variables_llm1,
-    inputs=[],
-    outputs="text",
-    title="Reset Variables for LLM 1"
-)
-
-
-# Gradio interface for updating LLM 2 variables and model
-llm2_interface = gr.Interface(
-    fn=update_variables_and_model_llm2,
-    inputs=[
-        gr.Dropdown(choices=MODEL_CHOICES_LLM2, label="LLM 2 - OpenAI - Models", value=DEFAULT_LLM2_MODEL),  # Dropdown for LLM2
-        "text", "text"
-    ],
+# Initialize CoinGecko API for cryptocurrency data
+duckduckgo_search_tool = DuckDuckGoSearchRun()
+
+from langchain_google_genai import ChatGoogleGenerativeAI
+# Set up API keys and environment variables
+api_gemini = 'AIzaSyB4feCeVxvjfd6a6L1jtaV_NHUMSh0MGQk' # Replace with your actual API key
+os.environ["api_gemini"] = api_gemini
+# Define the Large Language Model (LLM)
+llm = ChatGoogleGenerativeAI(model="gemini-pro", verbose=True, temperature=0.1, google_api_key=api_gemini)
+
+def create_crewai_crypto_setup(crypto_symbol):
+    # Define the Main Research Agent for cryptocurrency analysis
+    research_agent = Agent(
+        role="Main Research Agent",
+        goal=f"Conduct thorough research on {crypto_symbol}, covering its market performance, technical analysis, and recent developments.",
+        backstory="An expert in cryptocurrency analysis, skilled in evaluating digital currencies based on technical indicators, market trends, and blockchain developments.",
+        verbose=True,
+        allow_delegation=False,
+        tools=[duckduckgo_search_tool],
+        llm=llm,
+    )
+
+    # Define the Report Formatting Agent for creating investment plans
+    report_formatting_agent = Agent(
+        role="Investment Plan Creator",
+        goal="Compile and format the researched information into a structured and actionable investment plan for the cryptocurrency.",
+        backstory="Specializes in synthesizing market analysis into clear, actionable investment strategies for cryptocurrency investors.",
+        verbose=True,
+        allow_delegation=False,
+        llm=llm,
+    )
+
+    # Define Tasks for the Main Research Agent focused on cryptocurrency
+    task1 = Task(
+        description=f"Analyze the price history and current market trends of {crypto_symbol}.",
+        agent=research_agent,
+    )
+
+    task2 = Task(
+        description=f"Conduct technical analysis on {crypto_symbol}, focusing on key indicators like RSI, MACD, and support/resistance levels.",
+        agent=research_agent,
+    )
+
+    task3 = Task(
+        description=f"Review recent news, developments, and community sentiment around {crypto_symbol}.",
+        agent=research_agent,
+    )
+
+    task4 = Task(
+        description=f"Identify potential investment opportunities and risks for {crypto_symbol}.",
+        agent=research_agent,
+    )
+
+    # Define Task for the Report Formatting Agent to create a comprehensive investment plan
+    task5 = Task(
+        description=f"""Create a detailed investment plan for {crypto_symbol}, incorporating price targets, technical analysis, investment strategies, and timelines. 
+        Ensure the plan includes actionable advice on when to buy, sell, and hold, along with risk management strategies.""",
+        agent=report_formatting_agent,
+    )
+    task6 = Task(
+        description=f"""Condense the detailed investment plan for {crypto_symbol} into a concise, note-like format. Ensure the output highlights key price targets, technical indicators, and strategic advice within a 2000-character limit. Focus on succinctness and clarity, preserving the most critical investment insights.""",
+        agent=report_formatting_agent,
+    )
+
+    # Update the Crew to include the new condensing task
+    crypto_crew = Crew(
+        agents=[research_agent, report_formatting_agent],
+        tasks=[task1, task2, task3, task4, task5, task6],  # Include the new task in the task list
+        verbose=2,
+        process=Process.sequential,
+    )
+
+
+    crew_result = crypto_crew.kickoff()
+    return crew_result
+
+# Gradio Interface for the cryptocurrency investment plan app
+def run_crewai_crypto_app(crypto_symbol):
+    crew_result = create_crewai_crypto_setup(crypto_symbol)
+    return crew_result
+
+iface = gr.Interface(
+    fn=run_crewai_crypto_app, 
+    inputs="text", 
     outputs="text",
-    title="Update Variables and Model for LLM 2"
+    title="CrewAI Cryptocurrency Investment Plan",
+    description="Enter a cryptocurrency symbol to analyze and generate a comprehensive investment plan."
 )
-reset_llm2_interface = gr.Interface(
-    fn=reset_variables_llm2,
-    inputs=[],
-    outputs="text",
-    title="Reset Variables for LLM 2"
-)
-
-# Update Gradio chat interface
-chat_interface = gr.Interface(
-    fn=chat_with_llms, 
-    inputs=["text"], 
-    outputs="text", 
-    examples=[["Hello!"]]
-).queue()
 
-# Tabbed interface
-gr.TabbedInterface(
-    [chat_interface, llm1_interface, reset_llm1_interface, llm2_interface, reset_llm2_interface],
-    ["Chat", "LLM 1 - PPX", "Reset LLM 1", "LLM 2 - OpenAI", "Reset LLM 2"]
-).launch()
+iface.launch()