Update app.py

app.py

@@ -1,65 +1,97 @@
-# app.py
-import streamlit as st
+# Install necessary libraries
+!pip install groqflow faiss-cpu pandas transformers matplotlib plotly
+
+# Import required modules
 import pandas as pd
 import numpy as np
+import faiss
+from groqflow import EmbeddingModel
+from transformers import pipeline
+import plotly.express as px
+
+# Set up Groq API key
+groqflow.api_key = "your_groq_api_key"
 
-def load_data(file):
-    try:
-        df = pd.read_csv(file)
-        return df
-    except Exception as e:
-        st.error(f"Error loading file: {e}")
-        return None
-
-def generate_summary(df):
-    summary = {
-        'Column': df.columns,
-        'Data Type': [str(df[col].dtype) for col in df.columns],
-        'Non-Null Count': df.notnull().sum().values,
-        'Unique Values': [df[col].nunique() for col in df.columns],
-        'Sample Value': [df[col].iloc[0] if len(df[col]) > 0 else None for col in df.columns]
+# Load the dataset
+def load_dataset():
+    data = {
+        "EmployeeID": [101, 102, 103],
+        "Name": ["John Doe", "Jane Smith", "Alice Johnson"],
+        "Role": ["Developer", "Manager", "Analyst"],
+        "Department": ["IT", "HR", "Finance"],
+        "KPIs": ["95% on-time delivery", "Improved team output", "Increased report accuracy"],
+        "Feedback": [
+            "Great coding skills, always meets deadlines.",
+            "Excellent leadership, improved team productivity.",
+            "Strong analytical skills, provides valuable insights."
+        ],
+        "Projects": ["Developed new feature X.", "Managed project Y successfully.", "Optimized process Z for efficiency."]
     }
-    return pd.DataFrame(summary)
+    return pd.DataFrame(data)
+
+df = load_dataset()
+
+# Initialize Groq embedding model
+model = EmbeddingModel()
+
+# Generate embeddings for the dataset
+performance_data = df["Feedback"] + " " + df["Projects"]  # Combine relevant fields
+embeddings = [model.encode(text) for text in performance_data]
 
-def generate_insights(df):
-    insights = []
+# Convert embeddings to a NumPy array
+embedding_matrix = np.array(embeddings)
 
-    # Example insights
-    if 'avg_training_score' in df.columns:
-        avg_score = df['avg_training_score'].mean()
-        insights.append(f"The average training score is {avg_score:.2f}. Consider additional training for employees below this score.")
+# Create a FAISS index and add embeddings
+dimension = embedding_matrix.shape[1]
+index = faiss.IndexFlatL2(dimension)
+index.add(embedding_matrix)
 
-    if 'length_of_service' in df.columns:
-        experienced_employees = len(df[df['length_of_service'] > 5])
-        insights.append(f"{experienced_employees} employees have more than 5 years of service. Consider them for leadership roles.")
+# Initialize summarization pipeline
+summarizer = pipeline("summarization")
 
-    if 'awards_won' in df.columns:
-        award_winners = df['awards_won'].sum()
-        insights.append(f"A total of {award_winners} awards have been won by employees.")
+# Function to handle user queries
+def query_performance(query):
+    # Generate embedding for the query
+    query_embedding = model.encode(query)
 
-    return insights
+    # Search for relevant rows
+    D, I = index.search(np.array([query_embedding]), k=5)  # Retrieve top 5 results
+    relevant_rows = df.iloc[I[0]]
 
-# Streamlit app
-st.title("Employee Performance Dashboard")
-st.markdown("Upload your cleaned dataset to generate insights and suggestions.")
+    # Prepare context for summarization
+    context = " ".join(relevant_rows["Feedback"] + " " + relevant_rows["Projects"])
 
-# File upload
-uploaded_file = st.file_uploader("Upload CSV File", type="csv")
+    # Generate a summary
+    summary = summarizer(context, max_length=100, min_length=50, do_sample=False)
+    return summary[0]['summary_text'], relevant_rows
 
-if uploaded_file is not None:
-    df = load_data(uploaded_file)
+# Visualization function
+def visualize_performance():
+    # Example: Bar chart for KPIs by Department
+    kpi_counts = df["Department"].value_counts()
+    fig = px.bar(kpi_counts, x=kpi_counts.index, y=kpi_counts.values, labels={'x': 'Department', 'y': 'Count'}, title="Performance Metrics by Department")
+    fig.show()
 
-    if df is not None:
-        st.markdown("### Dataset Preview")
-        st.dataframe(df.head())
+# Main interactive app
+def main():
+    print("Welcome to the Employee Performance Dashboard!")
+    while True:
+        print("\nOptions:\n1. View Dashboard\n2. Query Performance\n3. Exit")
+        choice = input("Enter your choice: ")
 
-        st.markdown("### Dataset Summary")
-        summary = generate_summary(df)
-        st.dataframe(summary)
+        if choice == "1":
+            visualize_performance()
+        elif choice == "2":
+            query = input("Enter your performance query: ")
+            summary, rows = query_performance(query)
+            print("\nSummary:\n", summary)
+            print("\nRelevant Data:\n", rows)
+        elif choice == "3":
+            print("Goodbye!")
+            break
+        else:
+            print("Invalid choice. Please try again.")
 
-        st.markdown("### Insights and Suggestions")
-        insights = generate_insights(df)
-        for insight in insights:
-            st.write(f"- {insight}")
-else:
-    st.info("Please upload a CSV file.")
+# Run the app
+if __name__ == "__main__":
+    main()