Update app.py

app.py

@@ -1,31 +1,71 @@
-import streamlit as st
+import os
+import numpy as np
 import pandas as pd
+import faiss
+from sentence_transformers import SentenceTransformer
 from groq import Groq
 
+# Load pre-trained Sentence Transformer model
+model = SentenceTransformer('all-MiniLM-L6-v2')
+
 # Initialize Groq API client
 GROQ_API_KEY = "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"
 client = Groq(api_key=GROQ_API_KEY)
 
-# Helper functions
-def preprocess_data(uploaded_file):
-    data = pd.read_csv(uploaded_file)
-    return data
-
-def generate_report(data, query):
-    # This should include the retrieval and report generation logic from above
-    return f"Report for query: {query}"
-
-# Streamlit UI
-st.title("Energy Usage Analysis Report Generator")
-uploaded_file = st.file_uploader("Upload your CSV file", type=["csv"])
-
-if uploaded_file:
-    data = preprocess_data(uploaded_file)
-    st.write("Dataset Preview:")
-    st.dataframe(data.head())
-
-    query = st.text_input("Enter your query:")
-    if query:
-        report = generate_report(data, query)
-        st.write("Generated Report:")
-        st.text(report)
+# Generate embeddings using Sentence Transformers
+def generate_embeddings(text):
+    return model.encode(text)
+
+# Build FAISS index for retrieval
+def build_faiss_index(data):
+    index = faiss.IndexFlatL2(384)  # 384-dimensional embeddings for MiniLM
+    embeddings = [generate_embeddings(row.to_string()) for _, row in data.iterrows()]
+    embeddings = np.array(embeddings).astype("float32")
+    index.add(embeddings)
+    return index, embeddings
+
+# Query FAISS index
+def query_index(query, data, index):
+    query_embedding = generate_embeddings(query).astype("float32")
+    distances, indices = index.search(np.array([query_embedding]), k=5)
+    results = data.iloc[indices[0]]
+    return results
+
+# Generate a detailed report using Groq's generative model
+def generate_report_with_groq(query, results):
+    input_text = f"Based on the query '{query}', the following insights are generated:\n\n{results.to_string(index=False)}"
+    response = client.chat.completions.create(
+        messages=[{"role": "user", "content": input_text}],
+        model="llama3-8b-8192",
+        stream=False
+    )
+    return response.choices[0].message.content
+
+# Main function to execute the workflow
+if __name__ == "__main__":
+    # Load dataset
+    csv_path = "energy_usage_data.csv"  # Ensure this CSV is uploaded to your working directory
+    data = pd.read_csv(csv_path)
+
+    # Preprocess data (if needed)
+    data.fillna("", inplace=True)
+
+    # Build FAISS index
+    print("Building FAISS index...")
+    index, embeddings = build_faiss_index(data)
+
+    # User query
+    query = "Show households with high energy usage in the North region"
+    print(f"User Query: {query}")
+
+    # Query FAISS index
+    print("Retrieving relevant data...")
+    results = query_index(query, data, index)
+
+    # Generate report
+    print("Generating report using Groq API...")
+    report = generate_report_with_groq(query, results)
+
+    # Output the report
+    print("Generated Report:\n")
+    print(report)