tests.py

from mcp.server.fastmcp import FastMCP
import random 
import time
from litellm import completion
import shlex
from subprocess import Popen, PIPE
from threading import Timer
import os
import glob
import http.client
import json
import openpyxl
import shutil
from google import genai

client = genai.Client(api_key="AIzaSyDtP05TyoIy9j0uPL7_wLEhgQEE75AZQSc")

source_dir = "/app/uploads/temp"
destination_dir = "/app/code_interpreter"
files_list=[]
downloaded_files=[]
# os.environ.get('GROQ_API_KEY')
os.environ["GROQ_API_KEY"] ="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"
os.environ["GEMINI_API_KEY"] ="AIzaSyAQgAtQPpY0bQaCqCISGxeyF6tpDePx-Jg"
os.environ["OPENROUTER_API_KEY"] = "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"
mcp = FastMCP("code_sandbox")
data={}
result=""
stdout=""
stderr=""
import requests
import os
from bs4 import BeautifulSoup  # For parsing HTML


def download_all_files(base_url, files_endpoint, download_directory):
    """Downloads all files listed on the server's /upload page."""
    global downloaded_files

    # Create the download directory if it doesn't exist
    if not os.path.exists(download_directory):
        os.makedirs(download_directory)

    try:
        # 1. Get the HTML of the /upload page
        files_url = f"{base_url}{files_endpoint}"
        response = requests.get(files_url)
        response.raise_for_status()  # Check for HTTP errors

        # 2. Parse the HTML using BeautifulSoup
        soup = BeautifulSoup(response.content, "html.parser")

        # 3. Find all the <a> (anchor) tags, which represent the links to the files
        # This assumes the file links are inside <a> tags as shown in the server code
        file_links = soup.find_all("a")

        # 4. Iterate through the links and download the files
        for link in file_links:
            try:
                file_url = link.get("href")  # Extract the href attribute (the URL)
                if file_url:
                    # Construct the full file URL if the href is relative
                    if not file_url.startswith("http"):
                        file_url = f"{base_url}{file_url}"  #  Relative URLs

                    filename = os.path.basename(file_url) # Extract the filename from the URL
                    file_path = os.path.join(download_directory, filename)
                    if filename in downloaded_files:
                        pass
                    else:
                        downloaded_files.append(filename)
                        print(f"Downloading: {filename} from {file_url}")

                        # Download the file
                        file_response = requests.get(file_url, stream=True)  # Use stream=True for large files
                        file_response.raise_for_status() # Check for HTTP errors

                        with open(file_path, "wb") as file: # Open in binary write mode
                            for chunk in file_response.iter_content(chunk_size=8192): # Iterate and write in chunks (good for large files)
                                if chunk:  # filter out keep-alive new chunks
                                    file.write(chunk)

                        print(f"Downloaded: {filename} to {file_path}")

            except requests.exceptions.RequestException as e:
                print(f"Error downloading {link.get('href')}: {e}")
            except OSError as e: #Handles potential issues with file permissions or disk space.
                print(f"Error saving {filename}: {e}")

    except requests.exceptions.RequestException as e:
        print(f"Error getting file list from server: {e}")
    except Exception as e: # Catch all other potential errors
        print(f"An unexpected error occurred: {e}")

def transfer_files():
    for item in os.listdir(source_dir):
        item_path = os.path.join(source_dir, item)
        if os.path.isdir(item_path): # Check if it's a directory
            for filename in os.listdir(item_path):
                source_file_path = os.path.join(item_path, filename)
                destination_file_path = os.path.join(destination_dir, filename)
                shutil.move(source_file_path, destination_file_path)

def upload_file(file_path, upload_url):
    """Uploads a file to the specified server endpoint."""

    try:
        # Check if the file exists
        if not os.path.exists(file_path):
            raise FileNotFoundError(f"File not found: {file_path}")

        # Prepare the file for upload
        with open(file_path, "rb") as file:
            files = {"file": (os.path.basename(file_path), file)}  #  Important: Provide filename

            # Send the POST request
            response = requests.post(upload_url, files=files)

            # Check the response status code
            response.raise_for_status()  # Raise an exception for bad status codes (4xx or 5xx)

            # Parse and print the response
            if response.status_code == 200:
                print(f"File uploaded successfully.  Filename returned by server: {response.text}")
                return response.text # Return the filename returned by the server
            else:
                print(f"Upload failed. Status code: {response.status_code}, Response: {response.text}")
                return None

    except FileNotFoundError as e:
        print(e)
        return None  # or re-raise the exception if you want the program to halt
    except requests.exceptions.RequestException as e:
        print(f"Upload failed. Network error: {e}")
        return None


TOKEN = "5182224145:AAEjkSlPqV-Q3rH8A9X8HfCDYYEQ44v_qy0"
chat_id = "5075390513"
from requests_futures.sessions import FuturesSession
session = FuturesSession()

def run(cmd, timeout_sec):
    global stdout 
    global stderr
    proc = Popen(shlex.split(cmd), stdout=PIPE, stderr=PIPE,cwd="/app/code_interpreter/")
    timer = Timer(timeout_sec, proc.kill)
    try:
        timer.start()
        stdout, stderr = proc.communicate()
    finally:
        timer.cancel()


@mcp.tool()
def analyse_audio(audiopath,query) -> dict:
    """Ask another AI model about audios.The AI model can listen to the audio and give answers.Eg-query:Generate detailed minutes of meeting from the audio clip,audiopath='/app/code_interpreter/<audioname>'.Note:The audios are automatically present  in the /app/code_interpreter directory."""
    download_all_files("https://opengpt-4ik5.onrender.com", "/upload", "/app/code_interpreter")
    myfile = client.files.upload(file=audiopath)

    response = client.models.generate_content(
    model='gemini-2.0-flash',
    contents=[query, myfile]
    )
    return {"Output":str(response.text)}

@mcp.tool()
def analyse_video(videopath,query) -> dict:
    """Ask another AI model about videos.The AI model can see the videos and give answers.Eg-query:Create a very detailed transcript and summary of the video,videopath='/app/code_interpreter/<videoname>'Note:The videos are automatically present  in the /app/code_interpreter directory."""
    download_all_files("https://opengpt-4ik5.onrender.com", "/upload", "/app/code_interpreter")
    video_file = client.files.upload(file=videopath)

    while video_file.state.name == "PROCESSING":
        print('.', end='')
        time.sleep(1)
        video_file = client.files.get(name=video_file.name)

    if video_file.state.name == "FAILED":
        raise ValueError(video_file.state.name)

    response = client.models.generate_content(
    model='gemini-2.0-flash',
    contents=[query, video_file]
    )
    return {"Output":str(response.text)}


@mcp.tool()
def analyse_images(imagepath,query) -> dict:
    """Ask another AI model about images.The AI model can see the images and give answers.Eg-query:Who is the person in this image?,imagepath='/app/code_interpreter/<imagename>'.Note:The images are automatically present  in the /app/code_interpreter directory."""
    download_all_files("https://opengpt-4ik5.onrender.com", "/upload", "/app/code_interpreter")
    video_file = client.files.upload(file=imagepath)


    response = client.models.generate_content(
    model='gemini-2.0-flash',
    contents=[query, video_file]
    )
    return {"Output":str(response.text)}

@mcp.tool()
def create_code_files(filename: str, code: str) -> dict:
    global destination_dir
    download_all_files("https://opengpt-4ik5.onrender.com", "/upload", "/app/code_interpreter")
    """Create code files by passing the the filename as well the entire code to write.The file is created by default in the /app/code_interpreter directory.Note:All user uploaded files that you might need to work upon are stored in the /app/code_interpreter directory."""
    transfer_files()
    f = open(os.path.join(destination_dir, filename), "w")
    f.write(code)
    f.close()
    return {"info":"task completed. The referenced code files were created successfully. "}



@mcp.tool()
def run_code_files(start_cmd:str) -> dict:
    """(start_cmd:Example- sudo python /app/code_interpreter/app.py or bash /app/code_interpreter/app.py).The files must be inside the /app/code_interpreter directory."""
    global files_list
    global stdout
    global stderr
    run(start_cmd, 300)
    while stderr=="" and stdout=="":
        pass
    time.sleep(1.5)
    onlyfiles = glob.glob("/app/code_interpreter/*")
    onlyfiles=list(set(onlyfiles)-set(files_list))
    uploaded_filenames=[]
    for files in onlyfiles:
        try:
            uploaded_filename = upload_file(files, "https://opengpt-4ik5.onrender.com/upload")
            uploaded_filenames.append(f"https://opengpt-4ik5.onrender.com/static/{uploaded_filename}")
        except:
            pass
    files_list=onlyfiles
    return {"stdout":stdout,"stderr":stderr,"Files_download_link":uploaded_filenames}


@mcp.tool()
def run_shell_command(cmd:str) -> dict:
    """(cmd:Example- mkdir test.By default , the command is run inside the /app/code_interpreter/ directory.).Remember, the code_interpreter is running on **alpine linux** , so write commands accordingly.Eg-sudo does not work and is not required.."""
    global stdout
    global stderr

    run(cmd, 300)
    while stderr=="" and stdout=="":
        pass
    time.sleep(1.5)
    transfer_files()
    return {"stdout":stdout,"stderr":stderr}



@mcp.tool()
def install_python_packages(python_packages:str) -> dict:
    """python_packages to install seperated by space.eg-(python packages:numpy matplotlib).The following python packages are preinstalled:gradio XlsxWriter openpyxl"""
    global sbx
    package_names = python_packages.strip()
    command="pip install"
    if not package_names:
        return

    run(
        f"{command} --break-system-packages {package_names}", timeout_sec=300
    )
    while stderr=="" and stdout=="":
        pass
    time.sleep(2)
    return {"stdout":stdout,"stderr":stderr,"info":"Ran package installation command"}

@mcp.tool()
def get_youtube_transcript(videoid:str) -> dict:
    """Get the transcript of a youtube video by passing the video id.First search the web using google / exa for the relevant videos.Eg videoid=ZacjOVVgoLY"""
    conn = http.client.HTTPSConnection("youtube-transcript3.p.rapidapi.com")
    headers = {
        'x-rapidapi-key': "2a155d4498mshd52b7d6b7a2ff86p10cdd0jsn6252e0f2f529",
        'x-rapidapi-host': "youtube-transcript3.p.rapidapi.com"
    }
    conn.request("GET",f"/api/transcript?videoId={videoid}", headers=headers)

    res = conn.getresponse()
    data = res.read()
    return json.loads(data)

@mcp.tool()
def read_excel_file(filename) -> dict:
    """Reads the contents of an excel file.Returns a dict with key :value pair  = cell location:cell content.Always run this command first , when working with excels.The excel file is automatically present  in the /app/code_interpreter directory.Note:Always use openpyxl in python to work with excel files."""
    global destination_dir
    download_all_files("https://opengpt-4ik5.onrender.com", "/upload", "/app/code_interpreter")

    workbook = openpyxl.load_workbook(os.path.join(destination_dir, filename))

    # Create an empty dictionary to store the data
    excel_data_dict = {}

    # Iterate over all sheets
    for sheet_name in workbook.sheetnames:
        sheet = workbook[sheet_name]
        # Iterate over all rows and columns
        for row in sheet.iter_rows():
            for cell in row:
                # Get cell coordinate (e.g., 'A1') and value
                cell_coordinate = cell.coordinate
                cell_value = cell.value
                if cell_value is not None:
                    excel_data_dict[cell_coordinate] = str(cell_value)
    return excel_data_dict
@mcp.tool()
def scrape_websites(url_list:list,query:str) -> list:
    """Get the entire content of websites by passing in the url lists.query is the question you want to ask about the content of the website.e.g-query:Give .pptx links in the website.Note:Max urls in url_list is 3."""

    conn = http.client.HTTPSConnection("scrapeninja.p.rapidapi.com")


    headers = {
    'x-rapidapi-key': "2a155d4498mshd52b7d6b7a2ff86p10cdd0jsn6252e0f2f529",
    'x-rapidapi-host': "scrapeninja.p.rapidapi.com",
    'Content-Type': "application/json"
    }
    Output=[]
    for urls in url_list:
        payload = {"url" :urls} 
        payload=json.dumps(payload)
        conn.request("POST", "/scrape", payload, headers)
        res = conn.getresponse()
        data = res.read()
        content=str(data.decode("utf-8"))
        response = completion(
            model="gemini/gemini-2.0-flash-exp", 
            messages=[
            {"role": "user", "content": f"Output the following content in the human readable format.Try to conserve all the links and the text.Try to ouput the entire content.Remove the html codes so its human readable.Also answer this question about the content in a seperate paragraph:{query}.Here is the content:{content}"}
        ],
        )
        Output.append(response.choices[0].message.content)
        
    return {"website_content":Output}


@mcp.tool()
def deepthinking1(query:str,info:str) -> dict:
    """Ask another intelligent AI about the query.Ask the question defined by the query string and what you know about the question as well as provide your own knowledge and ideas about the question through the info string."""
    response = completion(
        model="groq/deepseek-r1-distill-llama-70b", 
        messages=[
        {"role": "user", "content": f"{query}.Here is what i Know about the query:{info}"}
    ],
        stream=False
    )


    return {"response":str(response.choices[0].message.content)}

@mcp.tool()
def deepthinking2(query:str,info:str) -> dict:
    """Ask another intelligent AI about the query.Ask the question defined by the query string and what you know about the question as well as provide your own knowledge and ideas about the question through the info string."""
    response = completion(
                model="openrouter/deepseek/deepseek-chat",
                messages=[
                {"role": "user", "content": f"Hi!"}],
                provider={"order": ["Together"],"allow_fallbacks":False},
                
            )


    return {"response":str(response.choices[0].message.content)}

@mcp.tool()
def deepthinking3(query:str,info:str) -> dict:
    """Ask another intelligent AI about the query.Ask the question defined by the query string and what you know about the question as well as provide your own knowledge and ideas about the question through the info string."""
    response = completion(
        model="gemini/gemini-2.0-flash-thinking-exp-01-21", 
        messages=[
        {"role": "user", "content": f"{query}.Here is what i Know about the query:{info}"}
    ],
    )


    return {"response":str(response.choices[0].message.content)}

if __name__ == "__main__":
    # Initialize and run the server
    mcp.run(transport='stdio')


# @mcp.tool()
# def run_website(start_cmd:str,port=8501) -> dict:
#     """(start_cmd:streamlit run app.py).Always specify sandbox id.Specify port (int) if different from 8501."""
#     output=sbx.commands.run(start_cmd,sandbox_id)
#     url = sbx.get_host(port)
#     info={"info":f"Your Application is live [here](https://{url})"}

#     return info