Update app.py

app.py

@@ -1,21 +1,21 @@
 import streamlit as st
 import os
-from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration
+import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
+from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration
 
-# Streamlit App
-st.title("SustainaBot: Solar Energy Advisor")
-st.sidebar.header("User Input")
-
-# Input for Groq API Key
 GROQ_API_KEY = "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"
 client = Groq(api_key = GROQ_API_KEY)
 
 # Initialize the RAG components
-tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
-retriever = RagRetriever.from_pretrained("facebook/rag-token-nq")
-model = RagSequenceForGeneration.from_pretrained("facebook/rag-token-nq")
+try:
+    tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
+    retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", use_dummy_dataset=True)
+    model = RagSequenceForGeneration.from_pretrained("facebook/rag-token-nq")
+except Exception as e:
+    st.error(f"Error loading RAG components: {e}")
+    st.stop()
 
 # Helper function for energy demand calculation
 def calculate_energy(appliances):
@@ -58,6 +58,16 @@ predefined_appliances = {
     "Electric Fan Heater": 1500
 }
 
+# Streamlit App
+st.title("SustainaBot: Solar Energy Advisor")
+st.sidebar.header("User Input")
+
+# Option for Groq API Key
+#groq_api_key = st.sidebar.text_input("Enter your Groq API Key", type="password")
+
+# Inputs
+st.sidebar.subheader("Enter Appliance Details")
+
 if "appliances" not in st.session_state:
     st.session_state.appliances = {}
 
@@ -97,52 +107,27 @@ if st.sidebar.button("Submit"):
     st.write(f"### Total Power Demand: {total_power:.2f} W")
 
     # RAG model interaction
-    prompt = f"Provide solar recommendations for a location: {location}, daily energy: {daily_energy:.2f} kWh, budget: {budget} USD."
-
-    # Tokenizing and retrieval process
-    inputs = tokenizer(prompt, return_tensors="pt")
-    question_input = inputs['input_ids']
-    doc_scores, docs = retriever(question_input)
-
-    # Generating the response
-    generated_output = model.generate(input_ids=question_input, context_input_ids=docs[0]['text'])
-    answer = tokenizer.decode(generated_output[0], skip_special_tokens=True)
-
-    st.write("### Solar Recommendations")
-    st.write(answer)
-
-    # Display potential savings and pricing (same as before)
+    try:
+        if not groq_api_key:
+            st.warning("No Groq API Key provided. RAG model will be used locally.")
+        else:
+            st.write("Using Groq API Key for enhanced recommendations.")
+
+        prompt = f"Provide solar recommendations for a location: {location}, daily energy: {daily_energy:.2f} kWh, budget: {budget} USD."
+        # Tokenizing and retrieval process
+        inputs = tokenizer(prompt, return_tensors="pt")
+        question_input = inputs['input_ids']
+        doc_scores, docs = retriever(question_input)
+
+        # Generating the response
+        generated_output = model.generate(input_ids=question_input, context_input_ids=docs[0]['text'])
+        answer = tokenizer.decode(generated_output[0], skip_special_tokens=True)
+
+        st.write("### Solar Recommendations")
+        st.write(answer)
+    except Exception as e:
+        st.error(f"Error during RAG model processing: {e}")
+
+    # Display potential savings and pricing
     savings = daily_energy * 30 * 0.2  # Assuming $0.2/kWh savings
     st.write(f"By switching to solar, you could save approximately **${savings:.2f} per month!**")
-
-    # Approximate Pricing Section
-    st.write("### Solar Installation Prices")
-    st.write("""
-    - **1 kW System:** Approx. PKR 120,000 - 150,000
-    - **3 kW System:** Approx. PKR 350,000 - 400,000
-    - **5 kW System:** Approx. PKR 600,000 - 700,000
-    - **10 kW System:** Approx. PKR 1,200,000 - 1,500,000
-    """)
-
-    # Visualization
-    st.write("### Appliance Breakdown")
-    if st.session_state.appliances:
-        appliance_data = pd.DataFrame([
-            {"Appliance": k, "Quantity": v['quantity'], "Daily Energy (kWh)": v['quantity'] * v['wattage'] * v['hours'] / 1000, "Power (W)": v['quantity'] * v['wattage']}
-            for k, v in st.session_state.appliances.items()
-        ])
-        st.table(appliance_data)
-
-        # Bar chart for visualization
-        st.write("### Energy Consumption by Appliance")
-        fig, ax = plt.subplots()
-        ax.bar(appliance_data["Appliance"], appliance_data["Daily Energy (kWh)"], color='skyblue')
-        ax.set_xlabel("Appliances")
-        ax.set_ylabel("Daily Energy (kWh)")
-        ax.set_title("Energy Consumption Breakdown")
-        plt.xticks(rotation=45, ha="right")
-        st.pyplot(fig)
-
-        # Highlight the most energy-consuming appliance
-        max_energy_app = appliance_data.loc[appliance_data["Daily Energy (kWh)"].idxmax()]
-        st.write(f"**Most Energy-Consuming Appliance:** {max_energy_app['Appliance']} consuming {max_energy_app['Daily Energy (kWh)']:.2f} kWh daily.")