Update app.py

app.py

@@ -1,4 +1,103 @@
 import os
+import requests
+import streamlit as st
+from groq import Groq
+
+# Set the Groq API key
+os.environ["GROQ_API_KEY"] = "key"
+
+# Initialize Groq client
+client = Groq(api_key=os.environ.get("key"))
+
+# Carbon footprint reduction data (kg CO2 per kg recycled)
+carbon_reduction_data = {
+    "Plastic Bottles": 3.8,
+    "Glass Bottles": 0.5,
+    "Metal Cans": 9.0,
+    "Old Clothes": 2.0,
+    "Paper and Cardboard": 1.3,
+    "E-Waste": 15.0,
+    "Tires": 8.0,
+}
+
+# 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. "
+        f"Suggest profitable and eco-friendly uses for {quantity} kg of {item}, "
+        f"including household uses, ways to monetize them, and calculate carbon footprint reduction. "
+        f"Keep your response to 5 points and add relevant emojis."
+    )
+    chat_completion = client.chat.completions.create(
+        messages=[{"role": "user", "content": prompt}],
+        model="llama-3.3-70b-versatile",
+        stream=False,
+    )
+    return chat_completion.choices[0].message.content
+
+# Function to generate images using Stable Diffusion API
+def generate_image(prompt):
+    api_url = "https://api-inference.huggingface.co/models/CompVis/stable-diffusion-v1-4"
+    headers = {"Authorization": f"Bearer your_huggingface_api_key"}
+    response = requests.post(api_url, headers=headers, json={"inputs": prompt})
+    if response.status_code == 200:
+        return response.content
+    else:
+        st.error("❗ Image generation failed. Please try again later.")
+        return None
+
+# App title
+st.title("♻️ Recycle-Smart-PK 🌍")
+st.write("Select clutter items, specify quantities, and get tailored, profitable recycling suggestions along with generated images!")
+
+# Multi-select input for clutter items
+selected_items = st.multiselect(
+    "Select items to recycle:", list(carbon_reduction_data.keys())
+)
+
+# Quantity input for selected items
+quantities = {}
+for item in selected_items:
+    quantities[item] = st.number_input(
+        f"Enter quantity for {item} (in kg):", min_value=0, step=1
+    )
+
+# Process and display results
+if st.button("Get Recycling Suggestions"):
+    if selected_items:
+        total_carbon_reduction = 0
+        st.write("### ♻️ Recycling Suggestions and Impact:")
+        for item, quantity in quantities.items():
+            if quantity > 0:
+                # Get text-based suggestions from Groq LLM
+                llm_response = get_recycling_suggestions_from_groq(item, quantity)
+
+                # Calculate carbon footprint reduction
+                carbon_reduction = carbon_reduction_data.get(item, 0) * quantity
+                total_carbon_reduction += carbon_reduction
+
+                # Generate image for the recycling suggestion
+                image_prompt = f"Visual representation of recycling {item} into eco-friendly and profitable products."
+                image = generate_image(image_prompt)
+
+                # Display text and image
+                st.write(f"**{item} ({quantity} kg)**")
+                st.write(llm_response)
+                st.write(f"🌍 **Carbon Footprint Reduction**: {carbon_reduction:.2f} kg CO₂")
+                if image:
+                    st.image(image, caption=f"Generated: {item}", use_column_width=True)
+                st.markdown("---")
+
+        # Display total carbon footprint reduction
+        st.write("### 🌟 Your Total Carbon Footprint Reduction 🌟")
+        st.write(f"🌍 **{total_carbon_reduction:.2f} kg CO₂ saved**")
+        st.success("🎉 Great job contributing to a greener planet!")
+    else:
+        st.error("❗ Please select at least one item and specify its quantity.")
+
+
+"""
+import os
 import streamlit as st
 from groq import Groq
 
@@ -93,3 +192,4 @@ if st.button("Ask and you will learn"):
         st.write(follow_up_response)
     else:
         st.error("Please enter a question.")
+    """