from flask import Flask, request, jsonify
import os
from langchain.prompts import ChatPromptTemplate
from langchain.agents import Tool, create_openai_tools_agent, AgentExecutor
from langchain_core.prompts import MessagesPlaceholder
from skills.vision import get_chat_completion
from skills.serch import search_tool
from skills.wiki import wikipedia_tool
from skills.control_mode import manual_control_mode , deactivate_manual_control_mode
from langchain_groq import ChatGroq
import requests

# Initialize the Groq API key
GROQ_API_KEY = "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"  # Replace with your actual API key
os.environ["GROQ_API_KEY"] = GROQ_API_KEY

# Initialize the Groq LLM
llm = ChatGroq(model_name="mixtral-8x7b-32768", temperature=0.7, max_tokens=4096)


# ____________________tools section_____________________





def analyse(query):

    response = get_chat_completion(query, image_file)
    return response


# Initialize external tools
tools = [
    Tool(
        name="analyseimage",
        func=analyse,
        description="Tool for image vision LLM model queries",
    ),
    Tool(
        name="Search",
        func=search_tool,
        description="Search the internet for current information",
    ),
    Tool(
        name="Wikipedia",
        func=wikipedia_tool,
        description="Query Wikipedia for detailed topic information",
    ),
    Tool(
        name="Manual remote Control",
        func=manual_control_mode,
        description="if user said activate manual  remote control mode this function needs to trigger ",
    ),
    Tool(
        name="deativate manualControl",
        func=deactivate_manual_control_mode,
        description="if user said deactivate manual remote control mode this function needs to trigger ",
    ),
    
]

# Create the agent prompt
template_messages = [
    (
        "system",
        "You are an AI assistant capable of using tools to analyze images, search the internet, and query Wikipedia.",
    ),
    ("user", "{input}"),
    MessagesPlaceholder(variable_name="agent_scratchpad"),
]
prompt = ChatPromptTemplate.from_messages(template_messages)

# Create the agent
agent = create_openai_tools_agent(llm=llm, tools=tools, prompt=prompt)

# Create the agent executor
agent_executor = AgentExecutor(
    agent=agent, tools=tools, verbose=True, handle_parsing_errors=True
)

# Initialize Flask app
app = Flask(__name__)


@app.route("/analyze", methods=["POST"])
def analyze():
    global image_file
    global query
    try:
        query = request.form.get("query")
        if not query:
            return jsonify({"error": "Query is required"}), 400
        image_file = request.files.get("image")

        response = agent_executor.invoke({"input": query})

        return jsonify({"response": response["output"]})

    except Exception as e:
        return jsonify({"error": f"Server error: {str(e)}"}), 500


# CORS Headers
@app.after_request
def after_request(response):
    response.headers.add("Access-Control-Allow-Origin", "*")
    response.headers.add("Access-Control-Allow-Headers", "Content-Type")
    response.headers.add("Access-Control-Allow-Methods", "GET,PUT,POST,DELETE,OPTIONS")
    return response


if __name__ == "__main__":
    app.run(debug=True, host="0.0.0.0", port=5000)