Update app.py

app.py

@@ -3,7 +3,7 @@ import json
 import tempfile
 import os
 import re # For parsing conversation
-from typing import Union, Optional, Dict # Import Dict
+from typing import Union, Optional, Dict, Tuple # Import Dict and Tuple
 # Import the actual functions from synthgen
 from synthgen import (
     generate_synthetic_text,
@@ -154,7 +154,7 @@ def generate_prompts_ui(
 
 # --- Modified Generation Wrappers ---
 
-# Wrapper for text generation + JSON preparation - RETURNS DICT
+# Wrapper for text generation + JSON preparation - RETURNS TUPLE
 def run_generation_and_prepare_json(
     prompt: str,
     model: str,
@@ -162,31 +162,18 @@ def run_generation_and_prepare_json(
     temperature: float,
     top_p: float,
     max_tokens: int
-) -> Dict[gr.Textbox, str]: # Return type hint (optional but good practice)
+) -> Tuple[gr.update, gr.update]: # Return type hint (optional)
     """Generates text samples and prepares a JSON file for download."""
     # Handle optional settings
     temp_val = temperature if temperature > 0 else None
     top_p_val = top_p if 0 < top_p <= 1 else None
     max_tokens_val = max_tokens if max_tokens > 0 else None
 
-    # Define component objects used in return dict keys - MUST MATCH OUTPUTS
-    # This requires the components to be defined *before* this function,
-    # which isn't the case. So we cannot use component objects as keys here.
-    # Gradio handles mapping if the keys are strings matching component labels
-    # OR if we return gr.update targeting components.
-    # Let's return explicit gr.update for clarity and robustness.
-
+    # Handle errors by returning updates for both outputs in a tuple
     if not prompt:
-        # Return updates for both outputs
-        return {
-            output_text: gr.update(value="Error: Please enter a prompt."),
-            download_file_text: gr.update(value=None) # Clear file output
-        }
+        return (gr.update(value="Error: Please enter a prompt."), gr.update(value=None))
     if num_samples <= 0:
-         return {
-            output_text: gr.update(value="Error: Number of samples must be positive."),
-            download_file_text: gr.update(value=None)
-        }
+         return (gr.update(value="Error: Number of samples must be positive."), gr.update(value=None))
 
     output_str = f"Generating {num_samples} samples using model '{model}'...\n"
     output_str += f"(Settings: Temp={temp_val}, Top-P={top_p_val}, MaxTokens={max_tokens_val})\n"
@@ -205,14 +192,11 @@ def run_generation_and_prepare_json(
     output_str += "="*20 + "\nGeneration complete (check results above for errors)."
     json_filepath = create_json_file(results_list, "text_samples.json")
 
-    # Return dictionary mapping components to updates
-    return {
-        output_text: gr.update(value=output_str),
-        download_file_text: gr.update(value=json_filepath) # Update file path
-    }
+    # Return tuple of updates in the order of outputs list
+    return (gr.update(value=output_str), gr.update(value=json_filepath))
 
 
-# Wrapper for conversation generation + JSON preparation - RETURNS DICT
+# Wrapper for conversation generation + JSON preparation - RETURNS TUPLE
 def run_conversation_generation_and_prepare_json(
     system_prompts_text: str,
     model: str,
@@ -220,32 +204,21 @@ def run_conversation_generation_and_prepare_json(
     temperature: float,
     top_p: float,
     max_tokens: int
-) -> Dict[gr.Textbox, str]: # Return type hint (optional)
+) -> Tuple[gr.update, gr.update]: # Return type hint (optional)
     """Generates conversations and prepares a JSON file for download."""
     temp_val = temperature if temperature > 0 else None
     top_p_val = top_p if 0 < top_p <= 1 else None
     max_tokens_val = max_tokens if max_tokens > 0 else None
 
-    # Define component objects used in return dict keys - requires components defined first.
-    # Using explicit gr.update instead.
-
+    # Handle errors by returning updates for both outputs in a tuple
     if not system_prompts_text:
-        return {
-            output_conv: gr.update(value="Error: Please enter or generate at least one system prompt/topic."),
-            download_file_conv: gr.update(value=None)
-        }
+        return (gr.update(value="Error: Please enter or generate at least one system prompt/topic."), gr.update(value=None))
     if num_turns <= 0:
-         return {
-            output_conv: gr.update(value="Error: Number of turns must be positive."),
-            download_file_conv: gr.update(value=None)
-        }
+         return (gr.update(value="Error: Number of turns must be positive."), gr.update(value=None))
 
     prompts = [p.strip() for p in system_prompts_text.strip().split('\n') if p.strip()]
     if not prompts:
-        return {
-            output_conv: gr.update(value="Error: No valid prompts found in the input."),
-            download_file_conv: gr.update(value=None)
-        }
+        return (gr.update(value="Error: No valid prompts found in the input."), gr.update(value=None))
 
     output_str = f"Generating {len(prompts)} conversations ({num_turns} turns each) using model '{model}'...\n"
     output_str += f"(Settings: Temp={temp_val}, Top-P={top_p_val}, MaxTokens={max_tokens_val})\n"
@@ -275,11 +248,8 @@ def run_conversation_generation_and_prepare_json(
     output_str += "="*40 + "\nGeneration complete (check results above for errors)."
     json_filepath = create_json_file(results_list_structured, "conversations.json")
 
-    # Return dictionary mapping components to updates
-    return {
-        output_conv: gr.update(value=output_str),
-        download_file_conv: gr.update(value=json_filepath)
-    }
+    # Return tuple of updates in the order of outputs list
+    return (gr.update(value=output_str), gr.update(value=json_filepath))
 
 
 # --- Gradio Interface Definition ---