Create appy.py

appy.py

@@ -0,0 +1,66 @@
+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.")