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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'
def get_bedrock_client():
return 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'
)
bedrock_runtime_client = get_bedrock_client()
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-haiku-20240307-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']
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-haiku-20240307-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
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):
response = bedrock_runtime_client.invoke_model(
modelId= "anthropic.claude-3-haiku-20240307-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
}
]
}
]
}))
response_body = json.loads(response.get("body").read())['content'][0]['text']
return response_body |