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app.py

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+import streamlit as st
+import json
+from transformers import pipeline
+
+@st.cache_resource
+def load_model(model_name):
+    return pipeline("text-generation", model=model_name)
+
+def main():
+    st.title("Prebid Module Generator")
+    st.write("Enter a Prebid module, such as 'appnexusBidAdapter', and get a generated Prebid installed module output starting from that setting onward. Using '[' will generate a Prebid config from the beginning. The model currently has a capped output of 1000 characters.")
+
+    st.subheader("Intended Uses")
+    st.write("This model is designed to assist publishers in understanding and exploring how most and advanced publishers configure their Prebid settings. It can serve as a valuable reference to gain insights into common configurations, best practices, and different approaches used by publishers across various domains. The model should be seen as a helpful tool to gain inspiration and understanding of common Prebid modules but not as a substitute for thorough testing and manual review of the final modules useds.")
+    st.write("To learn more about the default model, visit the [Prebid_Module_GPT2 model page](https://huggingface.co/PeterBrendan/Prebid_Module_GPT2). You can also refer to the [official Prebid Documentation on pbjs.setConfig](https://docs.prebid.org/dev-docs/publisher-api-reference/setConfig.html) for more information.")
+
+    st.write("*Note:* The model may take some time to generate the output.")
+
+    # Create a text input field for user prompt
+    user_input = st.text_input("Enter a Prebid module:", "")
+
+    # Check if the user input is empty
+    if user_input:
+        # Select the model
+        model_name = "PeterBrendan/Prebid_Module_GPT2"
+
+        # Load the Hugging Face model
+        generator = load_model(model_name)
+
+        # Display 'Generating Output' message
+        output_placeholder = st.empty()
+        with output_placeholder:
+            st.write("Generating Output...")
+
+        # Generate text based on user input
+        generated_text = generator(user_input, max_length=1000, num_return_sequences=1)[0]["generated_text"]
+
+        # Clear 'Generating Output' message and display the generated text
+        output_placeholder.empty()
+        st.write("Generated Text:")
+        try:
+            parsed_json = json.loads(generated_text)
+            beautified_json = json.dumps(parsed_json, indent=4)
+            st.code(beautified_json, language="json")
+        except json.JSONDecodeError:
+            st.write(generated_text)
+
+# Run the app
+if __name__ == "__main__":
+    main()

requirements.txt

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+streamlit==0.84.1
+transformers>=4.11.3
+torch>=1.9.0