app.py

# app.py

import streamlit as st
import torch
import torch.nn as nn
import torch.optim as optim
import onnx
import onnxruntime
import pandas as pd
from io import BytesIO, StringIO

# Define a simple neural network
class SimpleNN(nn.Module):
    def __init__(self):
        super(SimpleNN, self).__init__()
        self.fc = nn.Linear(28 * 28, 10)

    def forward(self, x):
        x = x.view(-1, 28 * 28)
        x = self.fc(x)
        return x

# Neural network for the CSV data
class EmbeddingNN(nn.Module):
    def __init__(self, num_libraries, num_descriptions, embedding_dim=10):
        super(EmbeddingNN, self).__init__()
        self.embedding = nn.Embedding(num_libraries, embedding_dim)
        self.fc = nn.Linear(embedding_dim, num_descriptions)
        
    def forward(self, x):
        x = self.embedding(x)
        x = self.fc(x)
        return x

@st.cache_data
def process_csv(csv_data):
    df = pd.read_csv(StringIO(csv_data))
    df['library_encoded'], library_classes = df['library_name'].factorize()
    df['description_encoded'], description_classes = df['description'].factorize()
    return df, library_classes, description_classes

def train_and_export(df):
    model = EmbeddingNN(len(df['library_encoded'].unique()), len(df['description_encoded'].unique()))
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters(), lr=0.001)
    
    for epoch in range(50):
        inputs = torch.tensor(df['library_encoded'].values, dtype=torch.long)
        labels = torch.tensor(df['description_encoded'].values, dtype=torch.long)
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
    
    buffer = BytesIO()
    torch.onnx.export(model, torch.tensor([0], dtype=torch.long), buffer)
    return buffer

def infer_from_onnx(model_buffer, library_name, library_classes, description_classes):
    byte_stream = BytesIO(model_buffer.getvalue())
    onnx_model = onnx.load(byte_stream)
    sess = onnxruntime.InferenceSession(byte_stream.getvalue())
    encoded_library = torch.tensor([list(library_classes).index(library_name)], dtype=torch.long)
    outputs = sess.run(None, {sess.get_inputs()[0].name: encoded_library.numpy()})
    predicted_description = description_classes[outputs[0].argmax()]
    return predicted_description

# Streamlit UI
st.title("PyTorch Neural Network Interface")

# Model Upload
uploaded_file = st.file_uploader("Choose an ONNX model file", type="onnx")
if uploaded_file:
    byte_stream = BytesIO(uploaded_file.getvalue())
    model = onnx.load(byte_stream)
    st.write("Model uploaded successfully!")

# Model Download
if st.button('Download Model as ONNX'):
    buffer = BytesIO()
    torch.onnx.export(SimpleNN(), torch.randn(1, 28, 28), buffer)
    st.download_button(
        label="Download ONNX model",
        data=buffer,
        file_name="model.onnx",
        mime="application/octet-stream"
    )

# Default CSV Example
DEFAULT_CSV = """
library_name,description
torch,PyTorch is an open-source machine learning library
tensorflow,Open source software library for high performance numerical computations
pandas,Data analysis and manipulation tool
numpy,Library for numerical computations in Python
"""

csv_data = st.text_area("Paste your CSV data here:", value=DEFAULT_CSV)

# Process CSV and cache the results
df, library_classes, description_classes = process_csv(csv_data)

if st.button('Convert CSV to ONNX Neural Net'):
    model_buffer = train_and_export(df)
    st.download_button(
        label="Download ONNX model",
        data=model_buffer,
        file_name="model.onnx",
        mime="application/octet-stream"
    )

# Inference
uploaded_model = st.file_uploader("Choose an ONNX model file for inference", type="onnx")
library_name_to_infer = st.text_input("Enter a library name for inference:")
if uploaded_model and library_name_to_infer:
    prediction = infer_from_onnx(uploaded_model, library_name_to_infer, library_classes, description_classes)
    st.write(f"Predicted description: {prediction}")