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import boto3
import json
#from IPython.display import clear_output, display, display_markdown, Markdown
import pandas as pd
from langchain.agents.agent_types import AgentType
from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent
from langchain_core.prompts import ChatPromptTemplate
from langchain_community.chat_models import BedrockChat
import streamlit as st
#from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
#import torch
region = 'us-east-1'
bedrock_runtime_client = boto3.client(
'bedrock-runtime',
aws_access_key_id=st.secrets['user_access_key'],
aws_secret_access_key=st.secrets['user_secret_key'], region_name = 'us-east-1'
)
# def generate_image_captions_ml():
# model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
# feature_extractor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
# tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# model.to(device)
# max_length = 16
# num_beams = 4
# gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
def invoke_model(input):
response = bedrock_runtime_client.invoke_model(
body=json.dumps({
'inputText': input
}),
modelId="amazon.titan-embed-text-v1",
accept="application/json",
contentType="application/json",
)
response_body = json.loads(response.get("body").read())
return response_body.get("embedding")
def invoke_model_mm(text,img):
body_ = {
"inputText": text,
}
if(img!='none'):
body_['inputImage']=img
body = json.dumps(body_)
modelId = 'amazon.titan-embed-image-v1'
accept = 'application/json'
contentType = "application/json"
response = bedrock_runtime_client.invoke_model(
body=body, modelId=modelId, accept=accept, contentType=contentType
)
response_body = json.loads(response.get("body").read())
#print(response_body)
return response_body.get("embedding")
def invoke_llm_model(input,is_stream):
if(is_stream == False):
response = bedrock_runtime_client.invoke_model(
modelId= "anthropic.claude-3-sonnet-20240229-v1:0",#"anthropic.claude-3-5-sonnet-20240620-v1:0",,
contentType = "application/json",
accept = "application/json",
body = json.dumps({
"anthropic_version": "bedrock-2023-05-31",
"max_tokens": 1024,
"temperature": 0.001,
"top_k": 250,
"top_p": 1,
"stop_sequences": [
"\n\nHuman:"
],
"messages": [
{
"role": "user",
"content":input
}
]
}
)
)
res = (response.get('body').read()).decode()
return (json.loads(res))['content'][0]['text']
# response = bedrock_runtime_client.invoke_model_with_response_stream(
# body=json.dumps({
# "prompt": input,
# "max_tokens_to_sample": 300,
# "temperature": 0.5,
# "top_k": 250,
# "top_p": 1,
# "stop_sequences": [
# "\n\nHuman:"
# ],
# # "anthropic_version": "bedrock-2023-05-31"
# }),
# modelId="anthropic.claude-v2:1",
# accept="application/json",
# contentType="application/json",
# )
# stream = response.get('body')
# return stream
# else:
# response = bedrock_runtime_client.invoke_model_with_response_stream(
# modelId= "anthropic.claude-3-sonnet-20240229-v1:0",
# contentType = "application/json",
# accept = "application/json",
# body = json.dumps({
# "anthropic_version": "bedrock-2023-05-31",
# "max_tokens": 1024,
# "temperature": 0.0001,
# "top_k": 150,
# "top_p": 0.7,
# "stop_sequences": [
# "\n\nHuman:"
# ],
# "messages": [
# {
# "role": "user",
# "content":input
# }
# ]
# }
# )
# )
# stream = response.get('body')
# return stream
def read_from_table(file,question):
print("started table analysis:")
print("-----------------------")
print("\n\n")
print("Table name: "+file)
print("-----------------------")
print("\n\n")
bedrock_params = {
"max_tokens":2048,
"temperature":0.0001,
"top_k":150,
"top_p":0.7,
"stop_sequences":["\\n\\nHuman:"]
}
model = BedrockChat(
client=bedrock_runtime_client,
model_id='anthropic.claude-3-sonnet-20240229-v1:0',
model_kwargs=bedrock_params,
streaming=False
)
if(str(type(file))=="<class 'str'>"):
df = pd.read_csv(file,skipinitialspace = True, on_bad_lines='skip',delimiter = "`")
else:
df = file
#df.fillna(method='pad', inplace=True)
agent = create_pandas_dataframe_agent(
model,
df,
verbose=True,
agent_executor_kwargs={'handle_parsing_errors':True,
'return_only_outputs':True},allow_dangerous_code = True
)
agent_res = agent.invoke(question)['output']
return agent_res
def generate_image_captions_llm(base64_string,question):
# ant_client = Anthropic()
# MODEL_NAME = "claude-3-opus-20240229"
# message_list = [
# {
# "role": 'user',
# "content": [
# {"type": "image", "source": {"type": "base64", "media_type": "image/jpeg", "data": base64_string}},
# {"type": "text", "text": "What is in the image ?"}
# ]
# }
# ]
# response = ant_client.messages.create(
# model=MODEL_NAME,
# max_tokens=2048,
# messages=message_list
# )
response = bedrock_runtime_client.invoke_model(
modelId= "anthropic.claude-3-sonnet-20240229-v1:0",
contentType = "application/json",
accept = "application/json",
body = json.dumps({
"anthropic_version": "bedrock-2023-05-31",
"max_tokens": 1024,
"messages": [
{
"role": "user",
"content": [
{
"type": "image",
"source": {
"type": "base64",
"media_type": "image/jpeg",
"data": base64_string
}
},
{
"type": "text",
"text": question
}
]
}
]
}))
#print(response)
response_body = json.loads(response.get("body").read())['content'][0]['text']
#print(response_body)
return response_body |