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import os
import streamlit as st
from transformers import pipeline, AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
def load_pipeline():
"""Load the Hugging Face pipeline for image classification."""
try:
return pipeline("image-classification", model="dima806/pneumonia_chest_xray_image_detection")
except Exception as e:
st.error(f"Error loading pipeline: {e}")
return None
def classify_image_with_pipeline(pipe, image):
"""Classify an image using the pipeline."""
try:
results = pipe(image)
return results
except Exception as e:
st.error(f"Error classifying image: {e}")
return None
# Streamlit App
st.title("Pneumonia Chest X-ray Image Detection")
st.markdown(
"""
This app detects signs of pneumonia in chest X-ray images using a pre-trained Hugging Face model.
"""
)
# File uploader
uploaded_file = st.file_uploader("Upload a chest X-ray image", type=["jpg", "jpeg", "png"])
if uploaded_file:
image = Image.open(uploaded_file)
st.image(image, caption="Uploaded Chest X-ray", use_column_width=True)
# Load the model pipeline
pipe = load_pipeline()
if pipe:
st.write("Classifying the image...")
results = classify_image_with_pipeline(pipe, image)
if results:
st.write("### Classification Results:")
for result in results:
st.write(f"**Label:** {result['label']} | **Score:** {result['score']:.4f}")
# Optional: Add Groq API integration if applicable
if os.getenv("GROQ_API_KEY"):
from groq import Groq
client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
st.sidebar.markdown("### Groq API Integration")
question = st.sidebar.text_input("Ask a question about pneumonia or X-ray diagnosis:")
if question:
try:
chat_completion = client.chat.completions.create(
messages=[
{
"role": "user",
"content": question,
}
],
model="llama-3.3-70b-versatile",
)
st.sidebar.write("**Groq API Response:**")
st.sidebar.write(chat_completion.choices[0].message.content)
except Exception as e:
st.sidebar.error(f"Error using Groq API: {e}")
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