Update app.py

app.py

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+import os
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import streamlit as st
+import requests
+
+# Groq API Setup
+API_KEY = "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"
+API_URL = "https://api.groq.com/openai/v1/chat/completions"  # Updated API URL
+
+def analyze_file(uploaded_file):
+    try:
+        # Load the file into a pandas DataFrame
+        if uploaded_file.name.endswith('.csv'):
+            df = pd.read_csv(uploaded_file.name)
+        elif uploaded_file.name.endswith('.xlsx'):
+            df = pd.read_excel(uploaded_file.name)
+        else:
+            return "Error: The uploaded file is neither CSV nor Excel."
+
+        # Clean up the column names by stripping any leading/trailing spaces
+        df.columns = df.columns.str.strip()
+
+        # Display the column names and first few rows for debugging
+        st.write("Columns in the uploaded file:", df.columns)
+        st.write("Preview of the uploaded data:", df.head())
+
+        # Check if required columns are present
+        if 'Gain (dB)' in df.columns and 'Frequency (GHz)' in df.columns:
+            # Perform basic data analysis
+            mean_gain = df['Gain (dB)'].mean()
+            median_gain = df['Gain (dB)'].median()
+            std_dev_gain = df['Gain (dB)'].std()
+
+            # Display analysis results
+            st.write(f"Mean Gain: {mean_gain}")
+            st.write(f"Median Gain: {median_gain}")
+            st.write(f"Standard Deviation of Gain: {std_dev_gain}")
+
+            # Plotting the data
+            fig, ax = plt.subplots()
+            ax.plot(df['Frequency (GHz)'], df['Gain (dB)'], label='Gain (dB)', color='blue')
+            ax.set_xlabel('Frequency (GHz)')
+            ax.set_ylabel('Gain (dB)')
+            ax.set_title('Gain vs Frequency')
+            ax.legend()
+            st.pyplot(fig)
+
+            # Send summary to Groq API for analysis
+            data_summary = f"""
+            The dataset contains simulation results for antennas. The frequency range is from 1 GHz to 10 GHz. 
+            - The antenna's gain increases from 5 dB to 30 dB as frequency increases. 
+            - Efficiency is consistently above 90%, with the highest reaching 99%.
+            """
+
+            headers = {
+                "Authorization": f"Bearer {API_KEY}",
+                "Content-Type": "application/json"
+            }
+
+            payload = {
+                "messages": [{"role": "user", "content": data_summary}],
+                "model": "llama-3.3-70b-versatile"  # Ensure this model is supported by Groq
+            }
+
+            # Send the request to Groq API
+            response = requests.post(API_URL, json=payload, headers=headers)
+            if response.status_code == 200:
+                groq_analysis = response.json()["choices"][0]["message"]["content"]
+            else:
+                groq_analysis = f"Error: {response.status_code}, {response.text}"
+
+            return mean_gain, median_gain, std_dev_gain, fig, groq_analysis
+        else:
+            return "Error: Required columns 'Gain (dB)' or 'Frequency (GHz)' not found in the dataset."
+
+    except Exception as e:
+        # Return error message if something goes wrong
+        return f"An error occurred: {str(e)}"
+
+
+# Streamlit Interface
+st.title("Data Analysis and Visualization with Groq API")
+st.write("Upload a CSV or Excel file to analyze the antenna data and get insights.")
+
+# File upload
+uploaded_file = st.file_uploader("Choose a file", type=["xlsx", "csv"])
+
+if uploaded_file is not None:
+    results = analyze_file(uploaded_file)
+    
+    if isinstance(results, tuple):  # If it's a valid result (tuple)
+        mean_gain, median_gain, std_dev_gain, fig, groq_analysis = results
+        
+        st.write(f"Mean Gain: {mean_gain}")
+        st.write(f"Median Gain: {median_gain}")
+        st.write(f"Standard Deviation of Gain: {std_dev_gain}")
+        
+        st.pyplot(fig)  # Show plot
+        
+        st.write("Groq's Analysis:")
+        st.write(groq_analysis)
+    else:
+        st.write(results)  # Error message