Update app.py

app.py

@@ -2,8 +2,8 @@ import streamlit as st
 import pandas as pd
 import matplotlib.pyplot as plt
 import seaborn as sns
-from groqflow.groqmodel import GroqModel  # Ensure this is the correct import for Groq model
-from groqflow.groqapi import GroqAPI  # Adjust this based on your Groq library
+import json
+import requests
 
 # Configure page
 st.set_page_config(page_title="Data Augmentation App", layout="wide")
@@ -27,18 +27,13 @@ st.sidebar.title("Upload Your File")
 st.sidebar.markdown("Supported formats: CSV, Excel")
 
 # Get the Groq API key from secrets
-groq_api_key = st.secrets.get("HUGGINGFACE_KEY")  # Ensure this is stored in secrets.json
+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:
-    try:
-        # Initialize the Groq API or Groq model client
-        groq_api = GroqAPI(api_key=groq_api_key)
-        groq_model = GroqModel(model="llama-3.1-8b-instant", groq_api=groq_api)  # Load the specific model
-        st.success("Groq model initialized successfully!")
-    except Exception as e:
-        st.error(f"Error initializing Groq model: {e}")
+    st.success("Groq API Key Loaded Successfully!")
 
+# Function to load data from file (CSV/Excel)
 def load_file(uploaded_file):
     """Load the uploaded file."""
     if uploaded_file.name.endswith('.csv'):
@@ -49,6 +44,7 @@ def load_file(uploaded_file):
         st.error("Unsupported file format. Please upload a CSV or Excel file.")
         return None
 
+# Function to generate graph based on user query
 def generate_graph(data, query):
     """Generate a graph based on user query."""
     try:
@@ -65,15 +61,36 @@ def generate_graph(data, query):
     except Exception as e:
         st.error(f"Error generating graph: {e}")
 
+# Function to handle user queries using the Groq model
 def handle_query(data, query):
     """Handle user query using the Groq model."""
     try:
-        if not groq_model:
-            st.error("Groq model is not initialized. Check for errors in setup.")
-            return
+        # 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}"
-        response = groq_model.generate_text(prompt)  # Use the Groq model's method for inference
-        st.write("Response:", response)
+        
+        # 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
+        }
+        
+        # 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}")
 
@@ -97,6 +114,7 @@ if uploaded_file:
             else:
                 handle_query(data, query)
 
+# Footer
 footer = """
     <div style='text-align: left; padding: 10px;'>
         <footer>Created by: Shamil Shahbaz</footer>