Formatted with PEP8.

app/gradio_sample_generator.py

@@ -1,4 +1,5 @@
 import json
+import tempfile
 import gradio as gr
 import pandas as pd
 from langchain_openai import ChatOpenAI
@@ -9,21 +10,43 @@ def examples_to_json(examples):
     pd_examples.columns = pd_examples.columns.str.lower()
     return pd_examples.to_json(orient="records")
 
-def process_json(examples, model_name, generating_batch_size, temperature):
+def process_json(
+    examples, model_name, generating_batch_size, temperature
+):
     try:
         # Convert the gradio dataframe into a JSON array
         input_json = examples_to_json(examples)
-        
-        model = ChatOpenAI(model=model_name, temperature=temperature, max_retries=3)
+
+        model = ChatOpenAI(
+            model=model_name, temperature=temperature, max_retries=3
+        )
         generator = TaskDescriptionGenerator(model)
         result = generator.process(input_json, generating_batch_size)
+
         description = result["description"]
-        examples_directly = [[example["input"], example["output"]] for example in result["examples_directly"]["examples"]]
+        examples_directly = [
+            [example["input"], example["output"]]
+            for example in result["examples_directly"]["examples"]
+        ]
         input_analysis = result["examples_from_briefs"]["input_analysis"]
         new_example_briefs = result["examples_from_briefs"]["new_example_briefs"]
-        examples_from_briefs = [[example["input"], example["output"]] for example in result["examples_from_briefs"]["examples"]]
-        examples = [[example["input"], example["output"]] for example in result["additional_examples"]]
-        return description, examples_directly, input_analysis, new_example_briefs, examples_from_briefs, examples
+        examples_from_briefs = [
+            [example["input"], example["output"]]
+            for example in result["examples_from_briefs"]["examples"]
+        ]
+        examples = [
+            [example["input"], example["output"]]
+            for example in result["additional_examples"]
+        ]
+
+        return (
+            description,
+            examples_directly,
+            input_analysis,
+            new_example_briefs,
+            examples_from_briefs,
+            examples,
+        )
     except Exception as e:
         raise gr.Error(f"An error occurred: {str(e)}")
     
@@ -47,48 +70,101 @@ def analyze_input(description, model_name, temperature):
     except Exception as e:
         raise gr.Error(f"An error occurred: {str(e)}")
     
-def generate_briefs(description, input_analysis, generating_batch_size, model_name, temperature):
+def generate_briefs(
+    description, input_analysis, generating_batch_size, model_name, temperature
+):
     try:
-        model = ChatOpenAI(model=model_name, temperature=temperature, max_retries=3)
+        model = ChatOpenAI(
+            model=model_name, temperature=temperature, max_retries=3
+        )
         generator = TaskDescriptionGenerator(model)
-        briefs = generator.generate_briefs(description, input_analysis, generating_batch_size)
+        briefs = generator.generate_briefs(
+            description, input_analysis, generating_batch_size
+        )
         return briefs
     except Exception as e:
         raise gr.Error(f"An error occurred: {str(e)}")
-    
-def generate_examples_from_briefs(description, new_example_briefs, examples, generating_batch_size, model_name, temperature):
+
+
+def generate_examples_from_briefs(
+    description, new_example_briefs, examples, generating_batch_size, model_name, temperature
+):
     try:
         input_json = examples_to_json(examples)
-
-        model = ChatOpenAI(model=model_name, temperature=temperature, max_retries=3)
+        model = ChatOpenAI(
+            model=model_name, temperature=temperature, max_retries=3
+        )
         generator = TaskDescriptionGenerator(model)
-        result = generator.generate_examples_from_briefs(description, new_example_briefs, input_json, generating_batch_size)
-        examples = [[example["input"], example["output"]] for example in result["examples"]]
+        result = generator.generate_examples_from_briefs(
+            description, new_example_briefs, input_json, generating_batch_size
+        )
+        examples = [
+            [example["input"], example["output"]]
+            for example in result["examples"]
+        ]
         return examples
     except Exception as e:
         raise gr.Error(f"An error occurred: {str(e)}")
-    
-def generate_examples_directly(description, raw_example, generating_batch_size, model_name, temperature):
+
+
+def generate_examples_directly(
+    description, raw_example, generating_batch_size, model_name, temperature
+):
     try:
         input_json = examples_to_json(raw_example)
         model = ChatOpenAI(model=model_name, temperature=temperature, max_retries=3)
         generator = TaskDescriptionGenerator(model)
-        result = generator.generate_examples_directly(description, input_json, generating_batch_size)
-        examples = [[example["input"], example["output"]] for example in result["examples"]]
+        result = generator.generate_examples_directly(
+            description, input_json, generating_batch_size
+        )
+        examples = [
+            [example["input"], example["output"]] for example in result["examples"]
+        ]
         return examples
     except Exception as e:
         raise gr.Error(f"An error occurred: {str(e)}")
 
+
 def format_selected_example(evt: gr.SelectData, examples):
     if evt.index[0] < len(examples):
-        selected_example = examples.iloc[evt.index[0]]  # Use iloc to access by integer position
-        json_example = json.dumps({"input": selected_example.iloc[0], "output": selected_example.iloc[1]}, indent=2, ensure_ascii=False)
+        selected_example = examples.iloc[evt.index[0]]
+        json_example = json.dumps(
+            {"input": selected_example.iloc[0], "output": selected_example.iloc[1]},
+            indent=2,
+            ensure_ascii=False,
+        )
         return json_example
     return ""
 
+def import_json(file):
+    if file is not None:
+        df = pd.read_json(file.name)
+        # Uppercase the first letter of each column name
+        df.columns = df.columns.str.title()
+        return df
+    return None
+
+def export_json(df):
+    if df is not None and not df.empty:
+        # Copy the dataframe and lowercase the column names
+        df_copy = df.copy()
+        df_copy.columns = df_copy.columns.str.lower()
+        
+        json_str = df_copy.to_json(orient="records", indent=2)
+
+        # create a temporary file with the json string
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".json") as temp_file:
+            temp_file.write(json_str.encode("utf-8"))
+            temp_file_path = temp_file.name
+
+        return temp_file_path
+    return None
+
 with gr.Blocks(title="Task Description Generator") as demo:
     gr.Markdown("# Task Description Generator")
-    gr.Markdown("Enter a JSON object with 'input' and 'output' fields to generate a task description and additional examples.")
+    gr.Markdown(
+        "Enter a JSON object with 'input' and 'output' fields to generate a task description and additional examples."
+    )
 
     with gr.Row():
         with gr.Column(scale=1):  # Inputs column
@@ -97,88 +173,193 @@ with gr.Blocks(title="Task Description Generator") as demo:
                 headers=["Input", "Output"],
                 datatype=["str", "str"],
                 row_count=(1, "dynamic"),
+                col_count=(2, "fixed"),
             )
+            json_file = gr.File(
+                label="Import/Export JSON", file_types=[".json"], type="filepath"
+            )
+            export_button = gr.Button("Export to JSON")
             model_name = gr.Dropdown(
                 label="Model Name",
-                choices=["llama3-70b-8192", "llama3-8b-8192", "llama-3.1-70b-versatile", "llama-3.1-8b-instant", "gemma2-9b-it"],
-                value="llama3-70b-8192"
+                choices=[
+                    "llama3-70b-8192",
+                    "llama3-8b-8192",
+                    "llama-3.1-70b-versatile",
+                    "llama-3.1-8b-instant",
+                    "gemma2-9b-it",
+                ],
+                value="llama3-70b-8192",
+            )
+            temperature = gr.Slider(
+                label="Temperature", value=1.0, minimum=0.0, maximum=1.0, step=0.1
+            )
+            generating_batch_size = gr.Slider(
+                label="Generating Batch Size", value=3, minimum=1, maximum=10, step=1
             )
-            temperature = gr.Slider(label="Temperature", value=1.0, minimum=0.0, maximum=1.0, step=0.1)
-            generating_batch_size = gr.Slider(label="Generating Batch Size", value=3, minimum=1, maximum=10, step=1)
             with gr.Row():
                 submit_button = gr.Button("Generate", variant="primary")
-                generate_description_button = gr.Button("Generate Description", variant="secondary")
+                generate_description_button = gr.Button(
+                    "Generate Description", variant="secondary"
+                )
 
         with gr.Column(scale=1):  # Outputs column
-            description_output = gr.Textbox(label="Description", lines=5, show_copy_button=True)
+            description_output = gr.Textbox(
+                label="Description", lines=5, show_copy_button=True
+            )
             with gr.Row():
-                generate_examples_directly_button = gr.Button("Generate Examples Directly", variant="secondary")
-                analyze_input_button = gr.Button("Analyze Input", variant="secondary")
-            examples_directly_output = gr.DataFrame(label="Examples Directly", headers=["Input", "Output"], interactive=False)
-            input_analysis_output = gr.Textbox(label="Input Analysis", lines=5, show_copy_button=True)
-            generate_briefs_button = gr.Button("Generate Briefs", variant="secondary")
-            example_briefs_output = gr.Textbox(label="Example Briefs", lines=5, show_copy_button=True)
-            generate_examples_from_briefs_button = gr.Button("Generate Examples from Briefs", variant="secondary")
-            examples_from_briefs_output = gr.DataFrame(label="Examples from Briefs", headers=["Input", "Output"], interactive=False)
-            examples_output = gr.DataFrame(label="Examples", headers=["Input", "Output"], interactive=False)
-            new_example_json = gr.Textbox(label="New Example JSON", lines=5, show_copy_button=True)
-
-            clear_button = gr.ClearButton([input_df, description_output, input_analysis_output,
-                                           example_briefs_output, examples_from_briefs_output,
-                                           examples_output, new_example_json])
+                generate_examples_directly_button = gr.Button(
+                    "Generate Examples Directly", variant="secondary"
+                )
+                analyze_input_button = gr.Button(
+                    "Analyze Input", variant="secondary"
+                )
+            examples_directly_output = gr.DataFrame(
+                label="Examples Directly",
+                headers=["Input", "Output"],
+                interactive=False,
+                datatype=["str", "str"],
+                row_count=(1, "dynamic"),
+                col_count=(2, "fixed"),
+            )
+            input_analysis_output = gr.Textbox(
+                label="Input Analysis", lines=5, show_copy_button=True
+            )
+            generate_briefs_button = gr.Button(
+                "Generate Briefs", variant="secondary"
+            )
+            example_briefs_output = gr.Textbox(
+                label="Example Briefs", lines=5, show_copy_button=True
+            )
+            generate_examples_from_briefs_button = gr.Button(
+                "Generate Examples from Briefs", variant="secondary"
+            )
+            examples_from_briefs_output = gr.DataFrame(
+                label="Examples from Briefs",
+                headers=["Input", "Output"],
+                interactive=False,
+                datatype=["str", "str"],
+                row_count=(1, "dynamic"),
+                col_count=(2, "fixed"),
+            )
+            examples_output = gr.DataFrame(
+                label="Examples",
+                headers=["Input", "Output"],
+                interactive=False,
+                datatype=["str", "str"],
+                row_count=(1, "dynamic"),
+                col_count=(2, "fixed"),
+            )
+            new_example_json = gr.Textbox(
+                label="New Example JSON", lines=5, show_copy_button=True
+            )
+
+            clear_button = gr.ClearButton(
+                [
+                    input_df,
+                    description_output,
+                    input_analysis_output,
+                    example_briefs_output,
+                    examples_from_briefs_output,
+                    examples_output,
+                    new_example_json,
+                ]
+            )
+
+    json_file.change(
+        fn=import_json,
+        inputs=[json_file],
+        outputs=[input_df],
+    )
+
+    export_button.click(
+        fn=export_json,
+        inputs=[input_df],
+        outputs=[json_file],
+    )
 
     submit_button.click(
         fn=process_json,
-        inputs=[input_df, model_name, generating_batch_size, temperature],  # Package first row
-        outputs=[description_output, examples_directly_output, input_analysis_output, example_briefs_output, examples_from_briefs_output, examples_output]
+        inputs=[
+            input_df,
+            model_name,
+            generating_batch_size,
+            temperature,
+        ],
+        outputs=[
+            description_output,
+            examples_directly_output,
+            input_analysis_output,
+            example_briefs_output,
+            examples_from_briefs_output,
+            examples_output,
+        ],
     )
 
     generate_description_button.click(
         fn=generate_description_only,
-        inputs=[input_df, model_name, temperature],  # Package first row
-        outputs=[description_output]
+        inputs=[input_df, model_name, temperature],
+        outputs=[description_output],
     )
 
     generate_examples_directly_button.click(
         fn=generate_examples_directly,
-        inputs=[description_output, input_df, generating_batch_size, model_name, temperature],  # Package first row
-        outputs=[examples_directly_output]
+        inputs=[
+            description_output,
+            input_df,
+            generating_batch_size,
+            model_name,
+            temperature,
+        ],
+        outputs=[examples_directly_output],
     )
 
     analyze_input_button.click(
         fn=analyze_input,
         inputs=[description_output, model_name, temperature],
-        outputs=[input_analysis_output]
+        outputs=[input_analysis_output],
     )
 
     generate_briefs_button.click(
         fn=generate_briefs,
-        inputs=[description_output, input_analysis_output, generating_batch_size, model_name, temperature],
-        outputs=[example_briefs_output]
+        inputs=[
+            description_output,
+            input_analysis_output,
+            generating_batch_size,
+            model_name,
+            temperature,
+        ],
+        outputs=[example_briefs_output],
     )
 
     generate_examples_from_briefs_button.click(
         fn=generate_examples_from_briefs,
-        inputs=[description_output, example_briefs_output, input_df, generating_batch_size, model_name, temperature],
-        outputs=[examples_from_briefs_output]
+        inputs=[
+            description_output,
+            example_briefs_output,
+            input_df,
+            generating_batch_size,
+            model_name,
+            temperature,
+        ],
+        outputs=[examples_from_briefs_output],
     )
 
     examples_directly_output.select(
         fn=format_selected_example,
         inputs=[examples_directly_output],
-        outputs=[new_example_json]
+        outputs=[new_example_json],
     )
 
     examples_from_briefs_output.select(
         fn=format_selected_example,
         inputs=[examples_from_briefs_output],
-        outputs=[new_example_json]
+        outputs=[new_example_json],
     )
 
     examples_output.select(
         fn=format_selected_example,
         inputs=[examples_output],
-        outputs=[new_example_json]
+        outputs=[new_example_json],
     )
 
     gr.Markdown("### Manual Flagging")
@@ -189,8 +370,15 @@ with gr.Blocks(title="Task Description Generator") as demo:
     flagging_callback = gr.CSVLogger()
     flag_button.click(
         lambda *args: flagging_callback.flag(args),
-        inputs=[input_df, model_name, generating_batch_size, description_output, examples_output, flag_reason],
-        outputs=[]
+        inputs=[
+            input_df,
+            model_name,
+            generating_batch_size,
+            description_output,
+            examples_output,
+            flag_reason,
+        ],
+        outputs=[],
     )
 
 if __name__ == "__main__":

guidelines/python.md

@@ -0,0 +1,11 @@
+# Python Guidelines
+
+## General Guidelines
+
+- Use 2 spaces for indentation.
+- Use snake_case for variable names.
+- Use camelCase for class names.
+- Use UPPER_CASE for constants.
+- Use triple quotes for multi-line strings.
+- Format with PEP 8 in mind.
+- Limit line length to 80 characters.