Update app.py

app.py

@@ -1,31 +1,84 @@
+# app.py
+
 import streamlit as st
 import torch
 import torch.nn as nn
 import torch.optim as optim
-import torchvision.transforms as transforms
-from PIL import Image
 import onnx
+import onnxruntime
+import pandas as pd
+from sklearn.preprocessing import LabelEncoder
 from io import BytesIO
 
+# Define a simple neural network
 class SimpleNN(nn.Module):
     def __init__(self):
         super(SimpleNN, self).__init__()
-        self.fc = nn.Linear(28 * 28, 10)  # Assuming 28x28 input and 10 classes
+        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
+
+def process_csv(csv_data):
+    df = pd.read_csv(StringIO(csv_data))
+    library_encoder = LabelEncoder()
+    description_encoder = LabelEncoder()
+    df['library_encoded'] = library_encoder.fit_transform(df['library_name'])
+    df['description_encoded'] = description_encoder.fit_transform(df['description'])
+    return df, library_encoder, description_encoder
+
+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_encoder, description_encoder):
+    byte_stream = BytesIO(model_buffer.getvalue())
+    onnx_model = onnx.load(byte_stream)
+    sess = onnxruntime.InferenceSession(byte_stream.getvalue())
+    encoded_library = library_encoder.transform([library_name])
+    outputs = sess.run(None, {sess.get_inputs()[0].name: encoded_library})
+    predicted_description = description_encoder.inverse_transform([outputs[0].argmax()])[0]
+    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)
@@ -36,5 +89,30 @@ if st.button('Download Model as 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
+scikit-learn,Machine learning library in Python
+"""
 
+csv_data = st.text_area("Paste your CSV data here:", value=DEFAULT_CSV)
+if st.button('Convert CSV to ONNX Neural Net'):
+    df, library_encoder, description_encoder = process_csv(csv_data)
+    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_encoder, description_encoder)
+    st.write(f"Predicted description: {prediction}")