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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import gradio as gr
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
print("Columns in the uploaded file:", df.columns)
print("Preview of the uploaded data:", df.head())
# Display column names and preview in the Gradio UI for debugging
st.write(df.columns)
st.write(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
print(f"Mean Gain: {mean_gain}")
print(f"Median Gain: {median_gain}")
print(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()
plt.close(fig) # Close to avoid inline display
# 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)}"
# Create the Gradio interface using the new Gradio 3.x syntax
iface = gr.Interface(
fn=analyze_file,
inputs=gr.File(label="Upload CSV or Excel File"), # Corrected to use `gr.File`
outputs=[
gr.Textbox(label="Mean Gain"),
gr.Textbox(label="Median Gain"),
gr.Textbox(label="Standard Deviation of Gain"),
gr.Image(label="Plot"),
gr.Textbox(label="Groq's Analysis")
],
title="Data Analysis and Visualization with Groq API",
description="Upload a CSV or Excel file to analyze the antenna data and get insights."
)
# Launch the interface
iface.launch()
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