Update app.py

app.py

@@ -1,132 +1,135 @@
-import streamlit as st
-from PIL import Image
-import random
-import torch
-from transformers import AutoImageProcessor, SiglipForImageClassification
-
-# Model from Hugging Face Hub (open-source)
-MODEL_NAME = "prithivMLmods/Recycling-Net-11"
-
-# Sustainability Tips List
-TIPS = [
-    "Rinse containers before recycling to avoid contamination.",
-    "Avoid using plastic bags for recyclables – use bins or boxes.",
-    "Compost your kitchen scraps instead of tossing them.",
-    "Recycle electronics only at designated e-waste centers.",
-    "Buy products made from recycled materials to close the loop.",
-    "Don’t recycle greasy pizza boxes – compost or trash them.",
-    "Learn your local recycling rules – they vary by region.",
-    "Use reusable bags, bottles, and containers to reduce waste.",
-    "Donate old clothes and furniture instead of throwing them away.",
-    "Avoid single-use plastics whenever possible.",
-]
-
-# Government Recycling Resources
-GOVERNMENT_LINKS = {
-    "Pakistan": "https://environment.gov.pk/",
-    "India": "https://www.cpcb.nic.in/",
-    "China": "http://english.mee.gov.cn/",
-    "Japan": "https://www.env.go.jp/en/",
-    "USA": "https://www.epa.gov/recycle",
-    "UK": "https://www.gov.uk/recycling-collections",
-    "Canada": "https://www.canada.ca/en/services/environment/conservation/recycling.html",
-    "Germany": "https://www.bmu.de/en/topics/water-waste-soil/waste-management",
-}
-
-# Load model and processor
-@st.cache_resource(show_spinner=False)
-def load_model():
-    processor = AutoImageProcessor.from_pretrained(MODEL_NAME)
-    model = SiglipForImageClassification.from_pretrained(MODEL_NAME)
-    model.eval()
-    return processor, model
-
-# Predict function
-def predict(image: Image.Image, processor, model):
-    inputs = processor(images=image, return_tensors="pt")
-    with torch.no_grad():
-        outputs = model(**inputs)
-    logits = outputs.logits
-    probs = torch.nn.functional.softmax(logits, dim=-1)
-    conf, idx = torch.max(probs, dim=-1)
-    class_name = model.config.id2label[idx.item()]
-    confidence = conf.item()
-    return class_name, confidence
-
-# Suggest recycling action based on prediction
-def get_suggestion(label: str) -> str:
-    tips = {
-        "aluminium": "Rinse and recycle aluminum cans. They are infinitely recyclable.",
-        "batteries": "Do not throw in the trash. Use proper e-waste collection centers.",
-        "cardboard": "Flatten and keep dry. Avoid greasy pizza boxes.",
-        "glass": "Rinse and remove lids. Separate by color if required.",
-        "hard plastic": "Check recycling codes. Clean before recycling.",
-        "paper": "Do not recycle shredded paper in curbside bins. Reuse or compost instead.",
-        "paper towel": "Compost if clean. Trash if soiled.",
-        "polystyrene": "Rarely accepted in curbside. Reuse or bring to special centers.",
-        "soft plastics": "Often require store drop-off. Don’t mix with other recyclables.",
-        "takeaway cups": "Check local rules. Many are lined and not recyclable curbside.",
-    }
-    return tips.get(label, "Please check your local rules for proper disposal of this item.")
-
-# Streamlit App
-def main():
-    st.set_page_config(page_title="♻️ Recycling Helper AI", layout="centered")
-    st.title("♻️ Recycling Helper AI")
-    st.subheader("An AI-powered app to identify recyclable materials and promote sustainability.")
-
-    st.markdown("---")
-
-    # Sidebar Content
-    with st.sidebar:
-        st.header("📘 About This App")
-        st.markdown(
-            "This open-source app helps you identify recyclable materials from waste images "
-            "using a machine learning model. It promotes proper disposal and reduces contamination "
-            "in the recycling stream. Built for hackathons using Hugging Face + Streamlit."
-        )
-
-        st.markdown("---")
-        st.header("🌐 Recycling Resources")
-        st.markdown("For proper recycling and disposal of waste, refer to the following resources:")
-        for country, url in GOVERNMENT_LINKS.items():
-            st.markdown(f"- [{country}]({url})", unsafe_allow_html=True)
-
-        st.markdown("---")
-        st.header("🌱 Daily Sustainability Tip")
-        tip = random.choice(TIPS)
-        st.success(tip)
-
-    # Load Model
-    processor, model = load_model()
-
-    # Image Upload
-    st.markdown("### 📤 Upload Waste Image")
-    uploaded_file = st.file_uploader("Upload an image of a recyclable item", type=["png", "jpg", "jpeg"])
-
-    if uploaded_file is not None:
-        try:
-            image = Image.open(uploaded_file).convert("RGB")
-            st.image(image, caption="Uploaded Image", use_column_width=True)
-
-            with st.spinner("Analyzing with AI model..."):
-                class_name, confidence = predict(image, processor, model)
-
-            st.success(f"**Predicted Material:** `{class_name}`  \n**Confidence:** `{confidence:.2%}`")
-
-            suggestion = get_suggestion(class_name)
-            st.info(f"**Tip:** {suggestion}")
-
-        except Exception as e:
-            st.error(f"Something went wrong: {e}")
-
-    # Expandable section to show class labels
-    with st.expander("🔍 Show Model Classes"):
-        st.write(model.config.id2label)
-
-    st.markdown("---")
-    st.caption("Made with 💚 for a sustainable future. | Hackathon 2025")
-
-# Run app
-if __name__ == "__main__":
-    main()
+import os
+os.environ["TRANSFORMERS_CACHE"] = "./hf_cache"  # Fix for cache permission issue
+
+import streamlit as st
+from PIL import Image
+import random
+import torch
+from transformers import AutoImageProcessor, SiglipForImageClassification
+
+# --- Constants ---
+MODEL_NAME = "prithivMLmods/Recycling-Net-11"
+
+# Sustainability Tips
+TIPS = [
+    "Rinse containers before recycling to avoid contamination.",
+    "Avoid using plastic bags for recyclables – use bins or boxes.",
+    "Compost your kitchen scraps instead of tossing them.",
+    "Recycle electronics only at designated e-waste centers.",
+    "Buy products made from recycled materials to close the loop.",
+    "Don’t recycle greasy pizza boxes – compost or trash them.",
+    "Learn your local recycling rules – they vary by region.",
+    "Use reusable bags, bottles, and containers to reduce waste.",
+    "Donate old clothes and furniture instead of throwing them away.",
+    "Avoid single-use plastics whenever possible.",
+]
+
+# Government recycling resources
+GOVERNMENT_LINKS = {
+    "Pakistan": "https://environment.gov.pk/",
+    "India": "https://www.cpcb.nic.in/",
+    "China": "http://english.mee.gov.cn/",
+    "Japan": "https://www.env.go.jp/en/",
+    "USA": "https://www.epa.gov/recycle",
+    "UK": "https://www.gov.uk/recycling-collections",
+    "Canada": "https://www.canada.ca/en/services/environment/conservation/recycling.html",
+    "Germany": "https://www.bmu.de/en/topics/water-waste-soil/waste-management",
+}
+
+# --- Load Model ---
+@st.cache_resource(show_spinner=False)
+def load_model():
+    processor = AutoImageProcessor.from_pretrained(MODEL_NAME)
+    model = SiglipForImageClassification.from_pretrained(MODEL_NAME)
+    model.eval()
+    return processor, model
+
+# --- Predict Function ---
+def predict(image: Image.Image, processor, model):
+    inputs = processor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    probs = torch.nn.functional.softmax(logits, dim=-1)
+    conf, idx = torch.max(probs, dim=-1)
+    class_name = model.config.id2label[idx.item()]
+    confidence = conf.item()
+    return class_name, confidence
+
+# --- Suggestion Function ---
+def get_suggestion(label: str) -> str:
+    tips = {
+        "aluminium": "Rinse and recycle aluminum cans. They are infinitely recyclable.",
+        "batteries": "Do not throw in the trash. Use proper e-waste collection centers.",
+        "cardboard": "Flatten and keep dry. Avoid greasy pizza boxes.",
+        "glass": "Rinse and remove lids. Separate by color if required.",
+        "hard plastic": "Check recycling codes. Clean before recycling.",
+        "paper": "Do not recycle shredded paper in curbside bins. Reuse or compost instead.",
+        "paper towel": "Compost if clean. Trash if soiled.",
+        "polystyrene": "Rarely accepted in curbside. Reuse or bring to special centers.",
+        "soft plastics": "Often require store drop-off. Don’t mix with other recyclables.",
+        "takeaway cups": "Check local rules. Many are lined and not recyclable curbside.",
+    }
+    return tips.get(label, "Please check your local rules for proper disposal of this item.")
+
+# --- Streamlit App ---
+def main():
+    st.set_page_config(page_title="♻️ Recycling Helper AI", layout="centered")
+    st.title("♻️ Recycling Helper AI")
+    st.subheader("An AI-powered app to identify recyclable materials and promote sustainability.")
+
+    st.markdown("---")
+
+    # Sidebar content
+    with st.sidebar:
+        st.header("📘 About This App")
+        st.markdown(
+            "This open-source app helps you identify recyclable materials from waste images "
+            "using a machine learning model. It promotes proper disposal and reduces contamination "
+            "in the recycling stream. Built for hackathons using Hugging Face + Streamlit."
+        )
+
+        st.markdown("---")
+        st.header("🌐 Recycling Resources")
+        st.markdown("For proper recycling and disposal of waste, refer to the following resources:")
+        for country, url in GOVERNMENT_LINKS.items():
+            st.markdown(f"- [{country}]({url})", unsafe_allow_html=True)
+
+        st.markdown("---")
+        st.header("🌱 Daily Sustainability Tip")
+        tip = random.choice(TIPS)
+        st.success(tip)
+
+    # Load the model
+    processor, model = load_model()
+
+    # File upload
+    st.markdown("### 📤 Upload Waste Image")
+    uploaded_file = st.file_uploader("Upload an image of a recyclable item", type=["png", "jpg", "jpeg"])
+
+    if uploaded_file is not None:
+        try:
+            image = Image.open(uploaded_file).convert("RGB")
+            st.image(image, caption="Uploaded Image", use_column_width=True)
+
+            with st.spinner("Analyzing with AI model..."):
+                class_name, confidence = predict(image, processor, model)
+
+            st.success(f"**Predicted Material:** `{class_name}`  \n**Confidence:** `{confidence:.2%}`")
+
+            suggestion = get_suggestion(class_name)
+            st.info(f"**Tip:** {suggestion}")
+
+        except Exception as e:
+            st.error(f"Something went wrong during prediction: {e}")
+
+    # Optional: Show class labels
+    with st.expander("🔍 Show Model Classes"):
+        st.write(model.config.id2label)
+
+    st.markdown("---")
+    st.caption("Made with 💚 for a sustainable future | Hackathon 2025")
+
+# Run app
+if __name__ == "__main__":
+    main()