Update streamlit_app.py

streamlit_app.py

@@ -1,64 +1,78 @@
 import streamlit as st
 import pandas as pd
+import requests
 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")
+# Page setup
+st.set_page_config(page_title="🧠 Excel SQL Assistant (Together AI)", layout="centered")
+st.title("📊 Excel to SQL with Together AI + Streamlit")
 
-# Load Groq API key from Streamlit secrets
-GROQ_API_KEY = st.secrets.get("GROQ_API_KEY", None)
+# API key from Hugging Face Secrets
+TOGETHER_API_KEY = st.secrets.get("TOGETHER_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.")
+if not TOGETHER_API_KEY:
+    st.error("❌ Together API key not found. Please add it in your Hugging Face Secrets.")
     st.stop()
 
-# Initialize Groq client
-client = Groq(api_key=GROQ_API_KEY)
-
-# Upload Excel file
+# File and input
 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")
+user_query = st.text_input("💬 Ask a question about your dataset")
 
-# Generate button
+# Process on button click
 if st.button("🚀 Generate & Run SQL"):
-    if uploaded_file is not None and user_query.strip() != "":
+    if uploaded_file is not None and user_query.strip():
         try:
+            # Load DataFrame
             df = pd.read_excel(uploaded_file)
-            st.success("✅ File successfully loaded!")
+            st.success("✅ File loaded successfully")
 
-            # 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 = f"""
+You are an expert data analyst. Given this sample dataset, write a Python pandas code snippet to answer the question.
+
+Data preview:
+{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}"""
+Question:
+{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}
+            # Make request to Together AI
+            with st.spinner("🧠 Generating response from Together AI..."):
+                headers = {
+                    "Authorization": f"Bearer {TOGETHER_API_KEY}",
+                    "Content-Type": "application/json"
+                }
+                payload = {
+                    "model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
+                    "max_tokens": 512,
+                    "temperature": 0.3,
+                    "messages": [
+                        {"role": "system", "content": "You are a helpful AI that writes pandas code from data questions."},
+                        {"role": "user", "content": prompt}
                     ]
+                }
+
+                response = requests.post(
+                    "https://api.together.xyz/v1/chat/completions",
+                    headers=headers,
+                    json=payload
                 )
 
-            answer = response.choices[0].message.content
-            st.code(answer, language="python")
+                if response.status_code != 200:
+                    raise ValueError(f"API error: {response.status_code} - {response.text}")
+
+                answer = response.json()['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}")
+                # Try running the generated code
+                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"⚠️ Couldn't execute generated code:\n{e}")
 
         except Exception as e:
             st.error(f"❌ Error processing file: {e}")