File size: 1,918 Bytes
89ef5a0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 |
# if you dont use pipenv uncomment the following:
# from dotenv import load_dotenv
# load_dotenv()
#Step1: Setup GROQ API key
import os
from dotenv import load_dotenv
load_dotenv()
GROQ_API_KEY=os.environ.get("GROQ_API_KEY")
#Step2: Convert image to required format
import base64
if GROQ_API_KEY is None:
raise ValueError("GROQ_API_KEY is not set! Add it to your environment or .env file.")
#image_path="D:/College/Major/ai-doctor-2.0-voice-and-vision/skin_rash.jpg"
def encode_image(image_path):
image_file=open(image_path, "rb")
return base64.b64encode(image_file.read()).decode('utf-8')
#Step3: Setup Multimodal LLM
from groq import Groq
model="meta-llama/llama-4-maverick-17b-128e-instruct"
def analyze_image_with_query(query, model, encoded_image):
client=Groq(api_key=GROQ_API_KEY)
messages=[
{
"role": "user",
"content": [
{
"type": "text",
"text": query
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{encoded_image}",
},
},
],
}]
chat_completion=client.chat.completions.create(
messages=messages,
model=model
)
return (chat_completion.choices[0].message.content)
def analyze_query(query, model):
client=Groq(api_key=GROQ_API_KEY)
messages=[
{
"role": "user",
"content": [
{
"type": "text",
"text": query
},
],
}]
chat_completion=client.chat.completions.create(
messages=messages,
model=model
)
return (chat_completion.choices[0].message.content) |