You are a highly Intelligent pyhton coder. Convert this code. Right now this code is reading images from a folder. I want you to modify this code and read csv file and convert that into a pandas dataframe.

from tqdm.autonotebook import tqdm
import json
import logging
import base64
import boto3
import time
import os
from botocore.exceptions import ClientError
# import pydantic_parser_IN
import pandas as pd
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
import re

def run_multi_modal_prompt(bedrock_runtime, model_id, messages, max_tokens):
    """
    Invokes a model with a multimodal prompt.
    Args:
        bedrock_runtime: The Amazon Bedrock boto3 client.
        model_id (str): The model ID to use.
        messages (JSON) : The messages to send to the model.
        max_tokens (int) : The maximum  number of tokens to generate.
    Returns:
        None.
    """

    body = json.dumps(
        {
            "anthropic_version": "bedrock-2023-05-31",
            "max_tokens": max_tokens,
            "messages": messages,
            "temperature" : 0,
            "top_p" :0.1,
            "top_k": 1        
            }
    )

    response = bedrock_runtime.invoke_model(
        body=body, modelId=model_id)
    response_body = json.loads(response.get('body').read())

    return response_body


def get_response(image_path):
    """
    Entrypoint for Anthropic Claude multimodal prompt example.
    """

    try:
        model_id = 'anthropic.claude-3-5-sonnet-20240620-v1:0'
        max_tokens = 3000
        input_image = image_path
        
        input_text = prompt_IN_conf
        # input_text= pydantic_parser_IN.get_prompt(prompt_IN)
 
        # Read reference image from file and encode as base64 strings.
        with open(input_image, "rb") as image_file:
            content_image = base64.b64encode(image_file.read()).decode('utf8')
            # content_image= base64.standard_b64encode(image_file.read()).decode('utf8')

        message = {"role": "user",
             "content": [
                {"type": "image", "source": {"type": "base64",
                    "media_type": "image/jpeg", "data": content_image}},
                {"type": "text", "text": input_text}
                ]}

    
        messages = [message]

        response = run_multi_modal_prompt(
            bedrock_runtime, model_id, messages, max_tokens)

        # print(json.dumps(response, indent=4))
        return response

    except ClientError as err:
        message = err.response["Error"]["Message"]
        logger.error("A client error occurred: %s", message)
        print("A client error occured: " +
              format(message))

folder_path = "/home/ritik1s/Desktop/K1 indian data/ k2_inv_new_entities/testing/"
file_name = []
response_usage_ip = []
response_usage_op = []
res_time = []
error_filename=[]
model_output=[]
df=pd.DataFrame()
json_output="/home/ritik1s/Desktop/Indian-Multi_pager-20231206T144616Z-001/Indian-Multi_pager/json output/"
for file in tqdm(os.listdir(folder_path)):
    image_path = os.path.join(folder_path,file)
    start_time = time.time()
    response = get_response(image_path)
    response_time = time.time() - start_time
    try:
        # json_response=pydantic_parser_IN.get_parsed_output(response=response['content'][0]['text'])     
        json_string =response['content'][0]['text']
        file_name.append(file)
        response_usage_ip.append(response['usage']['input_tokens'])
        response_usage_op.append(response['usage']['output_tokens'])
        res_time.append(response_time)
        # df['input']=df.apply(response_usage_ip)
        
        # output = json.loads(json_response)
        model_output.append(json_string)
        json_response = json_string.strip('"')
        json_response = json_response.replace('\\n', '').replace('\\', '')
        json_data = json.loads(json_response)
        print(json_data)
        # json_strings= str(json_string).replace("\n", "")
        # json_strings= str(json_string).replace('\"', '"\"')
        
        # model_output= str(model_output).replace('null', '"None"')
        json_file_path = str(image_path.split("/")[-1]) + ".json"
        with open(f'{json_output}/{json_file_path}', "w") as json_file:
            json.dump(json_data, json_file)    
        
        # print("Extraction completed for " , file)
        
    except Exception as err:
        error_filename.append(file)
        print(str(err))
        with open(f'{json_output}/{str(image_path.split("/")[-1]) + ".txt"}', "w") as f:
            f.write(str(response['content'][0]['text']))

        continue