Upload 2 files

app.py

@@ -0,0 +1,65 @@
+import gradio as gr
+import joblib
+import numpy as np
+import pandas as pd
+from openai import OpenAI
+from huggingface_hub import hf_hub_download
+
+# Load your pre-trained model and label names
+model_path = hf_hub_download(repo_id="govtech/zoo-entry-001", filename="model.joblib")
+model_data = joblib.load(model_path)
+model = model_data['model']
+label_names = model_data['label_names']
+
+# Initialize OpenAI client
+client = OpenAI()
+
+def get_embedding(text, embedding_model="text-embedding-3-large"):
+    """
+    Get embedding for the input text from OpenAI.
+    Replace newlines in the text, then call the API.
+    """
+    text = text.replace("\n", " ")
+    response = client.embeddings.create(
+        input=[text],
+        model=embedding_model
+    )
+    # Extract embedding vector from response
+    embedding = response.data[0].embedding
+    return np.array(embedding)
+
+def classify_text(text):
+    """
+    Get the OpenAI embedding for the provided text, classify it using your model,
+    and return an updated DataFrame component with the predictions and probabilities.
+    """
+    embedding = get_embedding(text)
+    # Add batch dimension
+    X = np.array(embedding)[None, :]
+    # Get probabilities from the model
+    probabilities = model.predict(X)
+    # Create a DataFrame with probabilities, labels, and binary predictions
+    df = pd.DataFrame({
+        'Label': label_names,
+        'Probability': probabilities[0],
+        'Prediction': (probabilities[0] > 0.5).astype(int)
+    })
+    # Return an update to the DataFrame component to make it visible with the results
+    return gr.update(value=df, visible=True)
+
+with gr.Blocks(title="Zoo Entry 001") as iface:
+    
+    with gr.Row():
+        input_text = gr.Textbox(lines=5, label="Input Text")
+    
+    with gr.Row():
+        submit_btn = gr.Button("Submit")
+    
+    # Initialize the table as hidden
+    with gr.Row():
+        output_table = gr.DataFrame(label="Classification Results", visible=False)
+    
+    submit_btn.click(fn=classify_text, inputs=input_text, outputs=output_table)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

@@ -0,0 +1,4 @@
+keras
+openai
+tensorflow
+joblib