Update app.py

app.py

@@ -10,7 +10,7 @@ import numpy as np
 os.environ["GROQ_API_KEY"] = "key"
 
 # Initialize Groq client
-client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+client = Groq(api_key=os.environ.get("key"))
 
 # Carbon footprint reduction data (kg CO2 per kg recycled)
 carbon_reduction_data = {
@@ -64,7 +64,7 @@ def load_segformer_model():
 
 segformer_extractor, segformer_model = load_segformer_model()
 
-# Function to call Groq LLM
+# Function to call Groq LLM for recycling suggestions
 def get_recycling_suggestions_from_groq(item, quantity):
     prompt = (
         f"You are an expert in recycling and sustainability. "
@@ -121,7 +121,65 @@ st.markdown(
     unsafe_allow_html=True,
 )
 
-if action == "Get Suggestions for Items":
+if action == "Upload Image":
+    st.markdown(
+        """
+        <div style="text-align: center; background-color: #e3f2fd; padding: 10px; border-radius: 5px;">
+            <h3 style="color: #01579b;">Upload an image of waste, and we'll identify items, suggest recycling ideas, and calculate carbon footprint reduction!</h3>
+        </div>
+        """,
+        unsafe_allow_html=True,
+    )
+    uploaded_image = st.file_uploader("Upload an image of the waste:", type=["jpg", "jpeg", "png"])
+
+    if uploaded_image:
+        image = Image.open(uploaded_image)
+        st.image(image, caption="Uploaded Image", use_container_width=True)
+
+        st.write("### YOLOv8: Detecting Waste Items...")
+        yolo_results = model.predict(image, conf=0.1)
+        yolo_detected_items = [model.model.names[int(pred[5])] for pred in yolo_results[0].boxes.data.tolist()]
+
+        st.write("### SegFormer: Analyzing Segmentation...")
+        segformer_inputs = segformer_extractor(images=image, return_tensors="pt")
+        segformer_outputs = segformer_model(**segformer_inputs)
+        segmentation_map = segformer_outputs.logits.argmax(dim=1).squeeze().numpy()
+        segformer_detected_items = [
+            ade20k_labels[class_id]
+            for class_id in np.unique(segmentation_map)
+            if class_id in ade20k_labels
+        ]
+
+        combined_items = set(yolo_detected_items + segformer_detected_items)
+
+        if combined_items:
+            st.write("### Combined Results:")
+            st.write(", ".join(combined_items))
+
+            total_carbon_reduction = 0
+            for item in combined_items:
+                st.markdown(f"**Recycling Idea for {item}:**")
+                response = get_recycling_suggestions_from_groq(item, 1)
+                carbon_reduction = max(0.5, min(2.5, carbon_reduction_data.get(item.lower(), 0) * 1))
+                total_carbon_reduction += carbon_reduction
+
+                st.write(response)
+                st.markdown(
+                    f"""<p style="color: #2e7d32;">🌍 Carbon Footprint Reduction: {carbon_reduction:.2f} kg CO₂</p>""",
+                    unsafe_allow_html=True,
+                )
+                st.write("---")
+
+            st.markdown(
+                f"""<div style="padding: 15px; text-align: center; background-color: #004d40; color: #ffffff; border-radius: 5px;">
+                    🌟 Total Carbon Footprint Reduction: <b>{total_carbon_reduction:.2f} kg CO₂ saved</b>
+                </div>""",
+                unsafe_allow_html=True,
+            )
+        else:
+            st.error("No recognizable waste items detected.")
+
+elif action == "Get Suggestions for Items":
     st.markdown(
         """
         <div style="text-align: center; background-color: #fff3e0; padding: 10px; border-radius: 5px;">