Create app_dev.py

app_dev.py

@@ -0,0 +1,46 @@
+import os
+import streamlit as st
+from transformers import pipeline
+
+# ... (other setup or imports you may need)
+
+# Pre-Configuration & Model Loading
+openchat_preprompt = """
+... (same content as before) ... 
+""" 
+
+def get_usernames(model: str):
+    # ... (Implementation remains identical) ... 
+    return preprompt, user_name, assistant_name, sep
+
+# Text Generation API Interaction (Modify as needed)
+def predict(model, inputs, typical_p, top_p, temperature, 
+            top_k, repetition_penalty, watermark, chatbot, history):
+    # ... (Logic adapted for your text generation API) ...
+
+# Streamlit UI 
+st.title("Large Language Model Chat API")
+st.markdown("**Description:** ... (Your description)")
+
+# Model Selection 
+model_name = st.radio("Model", [
+    "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
+    "OpenAssistant/oasst-sft-1-pythia-12b",
+    # ...  (other supported models) 
+])
+
+# Parameter Controls (Conditional display as in Gradio)
+# ...  (Streamlit controls for typical_p, top_p, etc.)
+
+# Chat Interface
+chatbot = st.empty()  # Placeholder for the chatbot display
+user_input = st.text_input("Type an input and press Enter", "")
+history = []
+
+if user_input:
+    history.append(user_input)
+    with st.spinner("Generating response..."):
+        chat_output, history = predict(model_name, user_input, # ... other parameters)
+        chatbot.text("\n".join([f"{data[0]}{data[1]}" for data in chat_output]))
+
+# ... (Rest of the UI, if needed) ...