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from huggingface_hub import InferenceClient
import os
# ---------------- PAGE CONFIG ----------------
st.set_page_config(page_title="Smart Waste Segregation Advisor")
st.title("♻️ AI-Powered Smart Waste Segregation Advisor")
st.subheader("SDG 12: Responsible Consumption and Production")
st.markdown("""
This tool helps users identify waste categories and provides guidance
for responsible disposal using AI-based reasoning powered by IBM Granite.
""")
# ---------------- USER INPUTS ----------------
uploaded_image = st.file_uploader(
"Upload an image of the waste item (optional)",
type=["jpg", "png", "jpeg"]
)
user_text = st.text_input("Optional: Describe the waste item")
st.divider()
# ---------------- ANALYZE BUTTON ----------------
if st.button("Analyze Waste"):
# Simulated image interpretation (Option 1 design choice)
if uploaded_image:
image_description = "An image showing a waste item uploaded by the user"
else:
image_description = "No image provided"
# Initialize IBM Granite via Hugging Face Inference API
client = InferenceClient(
model="ibm-granite/granite-4.0-mini",
token=os.getenv("HF_TOKEN")
)
prompt = f"""
You are an AI-powered Smart Waste Segregation Advisor.
Tasks:
1. Identify the waste item.
2. Classify it as one of the following:
- Wet Waste
- Dry Waste
- E-Waste
3. Explain briefly why it belongs to this category.
4. Suggest the correct and safe disposal method.
5. Explain the environmental impact if disposed of incorrectly.
User Input:
Image description: {image_description}
User text: {user_text}
"""
with st.spinner("Analyzing using IBM Granite AI..."):
response = client.chat_completion(
messages=[
{
"role": "system",
"content": "You are an AI assistant focused on sustainability and responsible waste management."
},
{
"role": "user",
"content": prompt
}
],
max_tokens=250,
temperature=0.7
)
ai_output = response.choices[0].message["content"]
st.markdown("### 🧠 AI Analysis Result")
st.write(ai_output)
|