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619fb4d | 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 71 | from dotenv import load_dotenv
load_dotenv()
import os
import base64
from groq import Groq
def encode_image(image_path: str) -> str:
"""
Reads an image file from disk and encodes it into a base64 string.
This is needed because the Groq multimodal API expects images
as base64 data URIs.
Args:
image_path (str): Path to the image file
Returns:
str: Base64-encoded image data
"""
with open(image_path, "rb") as f:
return base64.b64encode(f.read()).decode("utf-8")
def analyze_image_with_query(query: str, model: str, encoded_image: str) -> str:
"""
Sends a text query + an encoded image to Groq's multimodal chat completion API.
The LLM processes both modalities and returns a generated response.
Args:
query (str): The textual query/prompt (e.g., doctor's instruction)
model (str): The Groq multimodal model to use (e.g., llama-4-scout)
encoded_image (str): Base64-encoded image string
Returns:
str: The model's generated response
"""
# Fetch Groq API key from environment variables
api_key = os.environ.get("GROQ_API_KEY")
if not api_key:
raise RuntimeError("GROQ_API_KEY is not set in environment")
# Initialize Groq client with API key
client = Groq(api_key=api_key)
# Construct the multimodal message payload:
# - First element is plain text query
# - Second element is image encoded as a base64 data URL
messages = [
{
"role": "user",
"content": [
{"type": "text", "text": query},
{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{encoded_image}"}}
],
}
]
# Call Groq's chat completion API with low temperature (0.1) for deterministic output.
# Limit tokens to 1000 to control response length.
chat_completion = client.chat.completions.create(
messages=messages,
model=model,
temperature=0.1,
max_tokens=1000
)
# Extract and return the model's response text
return chat_completion.choices[0].message.content
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