Update app.py

app.py

@@ -1,12 +1,57 @@
 import gradio as gr
 
-class ConversionTool:
-    def __init__(self):
-        # Initialize widgets
-        self.intro = gr.Markdown(INTRODUCTION)
 
+INTRODUCTION="""
+
+### # Optimum CLI Export Tool.. tool
+
+This tool helps organize conversion commands when using Intel Optimum for Transformers and respects the order of positional arguments. Otherwise these commands can get quite nuanced to keep track of.
+
+My goal was to make it easier to construct commands for the  [Optimum CLI conversion tool](https://huggingface.co/docs/optimum/main/en/intel/openvino/export)  which enables converting models to the OpenVINO Intermediate Representation 
+outside of the from.pretrained method used in Transformers with OpenVINO related classes like OVModelForCausalLM, OVModelForSeq2SeqLM, OVModelForQuestionAnswering, etc, which interface with the OpenVINO runtime. 
+
+## Usage
+Here I'm assuming you have followed the instructions in the documentation and have all your dependencies in order.  
+
+Run to to get the latest version of the neccessary extension for optimum:
+```
+pip install --upgrade --upgrade-strategy eager optimum[openvino]
+```
+
+Intended workflow:
+	-Select conversion parameters.
+    -Hit "Submit"
+	-Copy command.
+	-Execute in your environment.
+
+Note: Converstion can take a while and will be resource intensive.
+
+    
+OpenVINO supports Intel CPUs from 6th gen forward, so you can squeeze performance out of older hardware with 
+different accuracy/performance tradeoffs than the popular quants of GGUFs. 
+                                 
+## Discussion
+
+Leveraging CPU, GPU and NPU hardware acceleration from OpenVINO requires converting a model into an Intermediate format derived from ONNX. 
+The command we execute rebuilds the model graph from it's source to be optimized for how OpenVINO uses this graph in memory. 
 
+Using OpenVINO effectively requires considering facts about your Intel hardware. Visit the [Intel Ark]([Intel® Processors for PC, Laptops, Servers, and AI | Intel®](https://www.intel.com/content/www/us/en/products/details/processors.html)) product database to find this information. 
 
+Here are some hardware questions you should be able to answer before using this tool;
+
+- What data types does my CPU support? 
+- What instruction sets?
+- How will I be using the model?
+- Do I have enough system memory for this task?
+
+    
+
+It's *the* ground truth for Intel Hardware specs. Even so, when testing with different model architectures 
+
+"""
+
+class ConversionTool:
+    def __init__(self):
 
         self.model_input = gr.Textbox(
             label='Model',
@@ -30,7 +75,8 @@ class ConversionTool:
                 'multiple-choice', 'depth-estimation', 'image-classification',
                 'fill-mask', 'zero-shot-object-detection', 'object-detection',
                 'question-answering', 'zero-shot-image-classification',
-                'mask-generation', 'text-generation', 'text-classification'
+                'mask-generation', 'text-generation', 'text-classification',
+                'text-to-text-generation', 'text-generation-with-past'
             ],
             value=None
         )
@@ -135,38 +181,36 @@ class ConversionTool:
         """Construct the command string"""
         if not model_input or not output_path:
             return ''
-        cmd_parts = ['optimum-cli export openvino']
         
-        # Required arguments
+
+        cmd_parts = ['optimum-cli export openvino']
         cmd_parts.append(f'-m "{model_input}"')
-        cmd_parts.append(f'"{output_path}"')
-            
-        # Optional arguments
-        if task != 'auto':
+
+        if task and task != 'auto':
             cmd_parts.append(f'--task {task}')
-            
-        if framework != 'auto':
+        
+        if framework:
             cmd_parts.append(f'--framework {framework}')
-                
-        if weight_format != 'fp32':
+            
+        if weight_format and weight_format != 'fp32':
             cmd_parts.append(f'--weight-format {weight_format}')
-                
-        if library != 'auto':
+            
+        if library and library != 'auto':
             cmd_parts.append(f'--library {library}')
-                
-        if ratio != 1.0:
+            
+        if ratio is not None and ratio != 0:
             cmd_parts.append(f'--ratio {ratio}')
-                
-        if group_size != 128:
+            
+        if group_size is not None and group_size != 0:
             cmd_parts.append(f'--group-size {group_size}')
-                
-        if backup_precision != 'int8_asym':
+            
+        if backup_precision:
             cmd_parts.append(f'--backup-precision {backup_precision}')
-                
-        if dataset != 'none':
+            
+        if dataset and dataset != 'none':
             cmd_parts.append(f'--dataset {dataset}')
-                
-        # Flags
+        
+        # Boolean flags - only add if True
         if trust_remote_code:
             cmd_parts.append('--trust-remote-code')
         if disable_stateful:
@@ -185,21 +229,24 @@ class ConversionTool:
             cmd_parts.append('--lora-correction')
         if sym:
             cmd_parts.append('--sym')
-            
-        # New optional arguments
+        
+        # Additional optional arguments - only add if they have values
         if quant_mode:
             cmd_parts.append(f'--quant-mode {quant_mode}')
         if cache_dir:
-            cmd_parts.append(f'--cache_dir {cache_dir}')
-        if pad_token_id:
+            cmd_parts.append(f'--cache_dir "{cache_dir}"')
+        if pad_token_id is not None and pad_token_id != 0:
             cmd_parts.append(f'--pad-token-id {pad_token_id}')
         if sensitivity_metric:
             cmd_parts.append(f'--sensitivity-metric {sensitivity_metric}')
-        if num_samples:
+        if num_samples is not None and num_samples != 0:
             cmd_parts.append(f'--num-samples {num_samples}')
-        if smooth_quant_alpha:
+        if smooth_quant_alpha is not None and smooth_quant_alpha != 0:
             cmd_parts.append(f'--smooth-quant-alpha {smooth_quant_alpha}')
-            
+
+        cmd_parts.append(f'"{output_path}"') 
+
+
         constructed_command = ' '.join(cmd_parts)
         return constructed_command
 
@@ -237,18 +284,13 @@ class ConversionTool:
             inputs=inputs,
             outputs=self.command_output,
             title="OpenVINO Conversion Tool",
-            description="Enter your model information to generate the `optimum-cli` command."
+            description="Enter model information to generate an `optimum-cli` export command.",
+            article=INTRODUCTION,
+            allow_flagging='auto'
         )
 
-        # Add custom CSS to make labels bold
-        interface.css = """
-        label {
-            font-weight: bold !important;
-        }
-        """
 
         return interface
-    
 
 if __name__ == "__main__":
     tool = ConversionTool()
@@ -257,51 +299,3 @@ if __name__ == "__main__":
 
 
 
-INTRODUCTION="""
-
-### # Optimum CLI Export Tool.. tool
-
-This tool helps organize conversion commands when using Intel Optimum for Transformers and respects the order of positional arguments. Otherwise these commands can get quite nuanced to keep track of.
-
-My goal was to make it easier to construct commands for the  [Optimum CLI conversion tool](https://huggingface.co/docs/optimum/main/en/intel/openvino/export)  which enables converting models to the OpenVINO Intermediate Representation 
-outside of the from.pretrained method used in Transformers with OpenVINO related classes like OVModelForCausalLM, OVModelForSeq2SeqLM, OVModelForQuestionAnswering, etc, which interface with the OpenVINO runtime. 
-
-## Usage
-Here I'm assuming you have followed the instructions in the documentation and have all your dependencies in order.  
-
-Run to to get the latest version of the neccessary extension for optimum:
-```
-pip install --upgrade --upgrade-strategy eager optimum[openvino]
-```
-
-Intended workflow:
-	-Select conversion parameters.
-    -Hit "Submit"
-	-Copy command.
-	-Execute in your environment.
-
-Note: Converstion can take a while and will be resource intensive.
-
-    
-OpenVINO supports Intel CPUs from 6th gen forward, so you can squeeze performance out of older hardware with 
-different accuracy/performance tradeoffs than the popular quants of GGUFs. 
-                                 
-## Discussion
-
-Leveraging CPU, GPU and NPU hardware acceleration from OpenVINO requires converting a model into an Intermediate format derived from ONNX. 
-The command we execute rebuilds the model graph from it's source to be optimized for how OpenVINO uses this graph in memory. 
-
-Using OpenVINO effectively requires considering facts about your Intel hardware. Visit the [Intel Ark ]([Intel® Processors for PC, Laptops, Servers, and AI | Intel®](https://www.intel.com/content/www/us/en/products/details/processors.html)) product database to find this information. 
-
-Here are some hardware questions you should be able to answer before using this tool;
-
-- What data types does my CPU support? 
-- What instruction sets?
-- How will I be using the model?
-- Do I have enough system memory for this task?
-
-    
-
-It's *the* ground truth for Intel Hardware specs. Even so, when testing with different model architectures 
-
-"""