Update app.py

app.py

@@ -2,8 +2,16 @@ import streamlit as st
 import pandas as pd
 import matplotlib.pyplot as plt
 import seaborn as sns
-import json
-import requests
+import os
+from groq import Groq
+
+# Check if openpyxl is installed
+try:
+    import openpyxl
+except ImportError:
+    st.error("openpyxl module is required to read Excel files. Attempting to install...")
+    os.system('pip install openpyxl')
+    st.success("openpyxl installed successfully. Please restart the app.")
 
 # Configure page
 st.set_page_config(page_title="Data Augmentation App", layout="wide")
@@ -26,13 +34,16 @@ st.title("Data Augmentation and Analysis App")
 st.sidebar.title("Upload Your File")
 st.sidebar.markdown("Supported formats: CSV, Excel")
 
-# Get the Groq API key from secrets
+# Fetch Groq API key from Streamlit secrets
 groq_api_key = st.secrets.get("HUGGINGFACE_KEY")  # Fetch Groq API key stored in secrets.json
 if not groq_api_key:
     st.error("Groq API key not found in secrets.")
 else:
     st.success("Groq API Key Loaded Successfully!")
 
+# Initialize Groq client
+client = Groq(api_key=groq_api_key)
+
 # Function to load data from file (CSV/Excel)
 def load_file(uploaded_file):
     """Load the uploaded file."""
@@ -62,35 +73,18 @@ def generate_graph(data, query):
         st.error(f"Error generating graph: {e}")
 
 # Function to handle user queries using the Groq model
-def handle_query(data, query):
+def handle_query(query):
     """Handle user query using the Groq model."""
     try:
-        # Groq API URL and Headers (adjust the URL to match your Groq API endpoint)
-        api_url = "https://api.groq.com/v1/models/llama-3.1-8b-instant/generate"
-        headers = {
-            "Authorization": f"Bearer {groq_api_key}",
-            "Content-Type": "application/json",
-        }
-        
-        # Create prompt for the model
-        prompt = f"Given the dataset: {data.to_dict(orient='records')}, answer the following: {query}"
+        # Send user query to Groq model using Groq SDK
+        chat_completion = client.chat.completions.create(
+            messages=[{"role": "user", "content": query}],
+            model="llama3-8b-8192",  # Model name as per your Groq model
+        )
         
-        # Prepare the request payload
-        payload = {
-            "inputs": prompt,
-            "max_tokens": 150,  # Adjust the response length if necessary
-            "temperature": 0.7  # Adjust temperature for randomness in response
-        }
+        # Display the response
+        st.write("Response from Groq Model:", chat_completion.choices[0].message.content)
         
-        # Send POST request to Groq API
-        response = requests.post(api_url, headers=headers, data=json.dumps(payload))
-        
-        # Check for successful response
-        if response.status_code == 200:
-            response_json = response.json()
-            st.write("Response from Groq Model:", response_json.get("generated_text", "No response"))
-        else:
-            st.error(f"Error querying Groq model: {response.status_code} - {response.text}")
     except Exception as e:
         st.error(f"Error in Groq processing: {e}")
 
@@ -109,10 +103,8 @@ if uploaded_file:
                 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)
+                handle_query(query)  # Use Groq model for query processing
 
 # Footer
 footer = """