Update app.py

app.py

@@ -2,6 +2,7 @@ import streamlit as st
 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")
@@ -9,29 +10,31 @@ 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.
+for responsible disposal using AI-based reasoning powered by IBM Granite.
 """)
 
-# Image upload
+# ---------------- USER INPUTS ----------------
 uploaded_image = st.file_uploader(
     "Upload an image of the waste item (optional)",
     type=["jpg", "png", "jpeg"]
 )
 
-# Optional text input
 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 HF Inference API
+    # Initialize IBM Granite via Hugging Face Inference API
     client = InferenceClient(
-        model="ibm-granite/granite-3.0-2b-instruct",
+        model="ibm-granite/granite-4.0-mini",
         token=os.getenv("HF_TOKEN")
     )
 
@@ -40,10 +43,13 @@ You are an AI-powered Smart Waste Segregation Advisor.
 
 Tasks:
 1. Identify the waste item.
-2. Classify it as Wet Waste, Dry Waste, or E-Waste.
-3. Explain the reason.
-4. Suggest correct disposal.
-5. Explain environmental impact.
+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}
@@ -51,11 +57,22 @@ User text: {user_text}
 """
 
     with st.spinner("Analyzing using IBM Granite AI..."):
-        response = client.text_generation(
-            prompt,
-            max_new_tokens=250,
+        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(response)
+    st.write(ai_output)