Update app.py

app.py

@@ -1,103 +1,4 @@
 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