Delete src/streamlit_app.py

src/streamlit_app.py

@@ -1,66 +0,0 @@
-import streamlit as st
-import pandas as pd
-import os
-from groq import Groq
-
-# Set page configuration
-st.set_page_config(page_title="📊 Excel SQL Assistant", layout="centered")
-st.title("🧠 Excel to SQL with GROQ + Streamlit")
-
-# Load Groq API key from Streamlit secrets
-GROQ_API_KEY = st.secrets.get("GROQ_API_KEY", None)
-
-# Fallback warning if API key not found
-if not GROQ_API_KEY:
-    st.error("❌ GROQ API key not found. Please set it in your Hugging Face `secrets` tab.")
-    st.stop()
-
-# Initialize Groq client
-client = Groq(api_key=GROQ_API_KEY)
-
-# Upload Excel file
-uploaded_file = st.file_uploader("📂 Upload your Excel file", type=["xlsx"])
-st.write("📁 Uploaded file object:", uploaded_file)  # Debugging aid
-
-# Text input for user query
-user_query = st.text_input("💬 Ask a question about your data")
-
-# Generate button
-if st.button("🚀 Generate & Run SQL"):
-    if uploaded_file is not None and user_query.strip() != "":
-        try:
-            df = pd.read_excel(uploaded_file)
-            st.success("✅ File successfully loaded!")
-
-            # Create schema-like string from the DataFrame
-            preview = df.head(5).to_string(index=False)
-            schema = f"Here are the first few rows of the dataset:\n\n{preview}"
-
-            # Prompt to Groq
-            full_prompt = f"""You are a data expert. Write a Pandas code snippet to answer the following question based on the data:\n\n{schema}\n\nQuestion: {user_query}"""
-
-            with st.spinner("⏳ Generating SQL-like response using Groq..."):
-                response = client.chat.completions.create(
-                    model="mixtral-8x7b-32768",
-                    messages=[
-                        {"role": "system", "content": "You are an expert data assistant that writes pandas code based on user questions."},
-                        {"role": "user", "content": full_prompt}
-                    ]
-                )
-
-            answer = response.choices[0].message.content
-            st.code(answer, language="python")
-
-            # Optionally: try to run it
-            try:
-                local_vars = {"df": df.copy()}
-                exec(answer, {}, local_vars)
-                if 'df' in local_vars:
-                    st.dataframe(local_vars['df'])
-            except Exception as e:
-                st.warning(f"⚠️ Could not execute the code:\n\n{e}")
-
-        except Exception as e:
-            st.error(f"❌ Error processing file: {e}")
-    else:
-        st.warning("⚠️ Please upload a file and enter a question.")