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import os
import gradio as gr
import pandas as pd
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
# ✅ Use DeepSeek Free Model
model_name = "deepseek-ai/deepseek-coder-6.7b"
# ✅ Load DeepSeek model & tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
# ✅ Function to analyze CSV data based on accuracy
def analyze_csv(file):
try:
df = pd.read_csv(file.name) # Read uploaded CSV
# ✅ Ensure column names are stripped of extra spaces
df.columns = df.columns.str.strip()
# ✅ Validate required columns
required_columns = {"Run ID", "Latency (ms)", "Throughput (req/sec)", "Memory Usage (GB)", "CPU Utilization (%)"}
if not required_columns.issubset(df.columns):
return f"Error: Missing one or more required columns. Required: {', '.join(required_columns)}"
# ✅ Avoid division errors (replace zero values in Latency & Memory Usage)
df["Latency (ms)"].replace(0, 1e-6, inplace=True)
df["Memory Usage (GB)"].replace(0, 1e-6, inplace=True)
# ✅ Calculate Accuracy Score: Throughput / (Latency * Memory Usage)
df["Accuracy Score"] = df["Throughput (req/sec)"] / (df["Latency (ms)"] * df["Memory Usage (GB)"])
# ✅ Find the best-performing model
best_model = df.loc[df["Accuracy Score"].idxmax()]
best_run_id = best_model["Run ID"]
# ✅ Construct analysis summary
summary = f"""
**🏆 Best Performing Test Run:** `{best_run_id}`
- **Latency:** {best_model["Latency (ms)"]} ms
- **Throughput:** {best_model["Throughput (req/sec)"]} req/sec
- **Memory Usage:** {best_model["Memory Usage (GB)"]} GB
- **CPU Utilization:** {best_model["CPU Utilization (%)"]}%
- **Accuracy Score:** {best_model["Accuracy Score"]:.6f}
---
**📊 Accuracy Ranking Table**
```plaintext
{df[["Run ID", "Accuracy Score"]].sort_values(by="Accuracy Score", ascending=False).to_string(index=False)}
```
---
Based on this benchmark, generate insights on why this test run performed best and provide recommendations.
"""
# ✅ Generate AI-based insights using DeepSeek
output = pipe(summary, max_new_tokens=150, do_sample=True, temperature=0.7)
return f"{summary}\n\n### 🤖 AI Insights:\n{output[0]['generated_text']}"
except Exception as e:
return f"⚠️ Error processing CSV: {str(e)}"
# ✅ Gradio Interface
iface = gr.Interface(
fn=analyze_csv,
inputs=gr.File(label="Upload CSV File"),
outputs="text",
title="Benchmark Analyzer (DeepSeek Free)",
description="Upload a benchmark CSV file to analyze test performance based on accuracy."
)
iface.launch() |