Added test code for app.py

app.py

@@ -1,16 +1,30 @@
 import gradio as gr
+import os
+import json
+import openai
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from run_llm import model_mapping, fastchat  # Import the necessary function from run_llm.py
 
-from transformers import pipeline
+# Set your OpenAI API key
+openai.api_key = "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"
 
-pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-en-es")
+def generate_text(input_text, model, prompt_type):
+    # Use the fastchat function from run_llm.py
+    outputs = fastchat(input_text, model, prompt_type)
+    return outputs
 
-def predict(text):
-  return pipe(text)[0]["translation_text"]
-
-demo = gr.Interface(
-  fn=predict,
-  inputs='text',
-  outputs='text',
+iface = gr.Interface(
+    fn=generate_text,
+    inputs=[
+        gr.Textbox("input_text", label="Input Text"),
+        gr.Dropdown(
+            list(model_mapping.keys()),
+            label="Model"
+        ),
+        gr.Radio([1, 2], label="Prompt Type"),
+    ],
+    outputs=gr.Textbox("output_text", label="Generated Text")
 )
 
-demo.launch()
+iface.launch()