Update app.py

app.py

@@ -48,14 +48,29 @@ def upload_model(model_id, tokenizer):
 
     return model_repo_name
 
-def process(model_id, dataset):
-    tokenizer = download_model(model_id)
-    model_repo_name = upload_model(model_id, tokenizer)
+def process(action, system_prompt, user_prompt, model_id, dataset):
+    if action == "Prompt base model":
+        result = "Prompt base model"
+    elif action == "Fine-tuned base model":
+        result = "Fine-tuned base model"
+    elif action == "Prompt fine-tuned model":
+        result = "Prompt fine-tuned model"
     
-    return model_repo_name
+    #tokenizer = download_model(model_id)
+    #model_repo_name = upload_model(model_id, tokenizer)
+    
+    return result
+
+system_prompt = "You are a text to SQL query translator. Users will ask you a question in English and you will generate a SQL query."
+user_prompt = "What is the total trade value and average price for each trader and stock in the trade_history table?"
+model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
+dataset = "gretelai/synthetic_text_to_sql"
 
 demo = gr.Interface(fn=process, 
-                    inputs=[gr.Textbox(label = "Model ID", value = "meta-llama/Meta-Llama-3-8B-Instruct", lines = 1),
-                            gr.Textbox(label = "Dataset", value = "gretelai/synthetic_text_to_sql", lines = 1)],
+                    inputs=[gr.Radio(["Prompt base model", "Fine-tune base model", "Prompt fine-tuned model"], label = "Action")],
+                            gr.Textbox(label = "System Prompt", value = system_prompt, lines = 1),
+                            gr.Textbox(label = "User Prompt", value = user_prompt, lines = 1),
+                            gr.Textbox(label = "Model ID", value = model_id, lines = 1),
+                            gr.Textbox(label = "Dataset", value = dataset, lines = 1)],
                     outputs=[gr.Textbox(label = "Completion")])
 demo.launch()