Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from groqflow.groqmodel import GroqModel
+
+# Configure page
+st.set_page_config(page_title="Data Augmentation App", layout="wide")
+st.markdown(
+    f"""
+    <style>
+        .reportview-container {{
+            background: url("https://cdn.pixabay.com/photo/2016/06/02/02/33/triangles-1430105_1280.png");
+            background-size: cover;
+        }}
+        footer {{
+            text-align: left;
+        }}
+    </style>
+    """,
+    unsafe_allow_html=True,
+)
+
+st.title("Data Augmentation and Analysis App")
+st.sidebar.title("Upload Your File")
+st.sidebar.markdown("Supported formats: CSV, Excel")
+
+# Load Groq API key from secrets
+groq_api_key = st.secrets["HUGGINGFACE_KEY"]
+
+# Load Groq model
+groq_model = GroqModel("llama3-8b-8192", api_key=groq_api_key)
+
+def load_file(uploaded_file):
+    """Load the uploaded file."""
+    if uploaded_file.name.endswith('.csv'):
+        return pd.read_csv(uploaded_file)
+    elif uploaded_file.name.endswith('.xlsx'):
+        return pd.read_excel(uploaded_file)
+    else:
+        st.error("Unsupported file format. Please upload a CSV or Excel file.")
+        return None
+
+def generate_graph(data, query):
+    """Generate a graph based on user query."""
+    try:
+        fig, ax = plt.subplots(figsize=(10, 6))
+        if "correlation" in query.lower():
+            sns.heatmap(data.corr(), annot=True, cmap="coolwarm", ax=ax)
+            st.pyplot(fig)
+        elif "histogram" in query.lower():
+            column = st.selectbox("Select a column for the histogram", data.columns)
+            sns.histplot(data[column], kde=True, ax=ax)
+            st.pyplot(fig)
+        else:
+            st.error("Unsupported graph type. Try asking for a correlation matrix or histogram.")
+    except Exception as e:
+        st.error(f"Error generating graph: {e}")
+
+def handle_query(data, query):
+    """Handle user query using Groq API."""
+    try:
+        prompt = f"Given the dataset: {data.to_dict(orient='records')}, answer the following: {query}"
+        response = groq_model.generate(prompt)
+        st.write("Response:", response)
+    except Exception as e:
+        st.error(f"Error in LLM processing: {e}")
+
+# Main App
+uploaded_file = st.sidebar.file_uploader("Upload your file here", type=["csv", "xlsx"])
+if uploaded_file:
+    data = load_file(uploaded_file)
+    if data is not None:
+        st.write("Dataset Preview")
+        st.dataframe(data)
+
+        query = st.text_area("Ask your question about the dataset")
+        if query:
+            if "table" in query.lower():
+                st.write("Table Preview")
+                st.write(data)
+            elif "graph" in query.lower():
+                generate_graph(data, query)
+            elif "predict" in query.lower():
+                st.write("Prediction functionality is in progress.")
+            else:
+                handle_query(data, query)
+
+footer = """
+    <div style='text-align: left; padding: 10px;'>
+        <footer>Created by: Shamil Shahbaz</footer>
+    </div>
+"""
+st.markdown(footer, unsafe_allow_html=True)