using basic data models

config.toml

@@ -9,4 +9,4 @@ REPOSITORY_LABEL = "OpenAI Domino Pieces"
 
 # The version of this Pieces release
 # Attention: changing this will create a new release 
-VERSION = "0.4.0"
+VERSION = "0.4.1"

dry_run_results/display_result.md

@@ -0,0 +1,9 @@
+
+## Generated prompt:
+As the divers descend into the depths of the ocean, they are greeted by a surrealistic underwater city, bathed in the ethereal glow of bioluminescent creatures. The ancient structures rise like coral formations, adorned with intricate, mysterious symbols that seem to whisper forgotten tales. In this mesmerizing realm, the explorers uncover evidence of an advanced civilization lost to time. The image you create should capture the awe-inspiring beauty of this underwater metropolis, blending vibrant colors and dreamlike imagery. Let the surrealistic oceanic exploration art style bring to life the secrets and wonders that lie beneath the waves.
+
+## Args
+**context**: Explorers dive into a mesmerizing underwater city, discovering ancient secrets, mysterious symbols, and evidence of an advanced civilization.
+**model**: gpt-3.5-turbo
+**temperature**: 0.7
+**completion_max_tokens**: 350

dry_run_results/generated_prompt.txt

@@ -0,0 +1 @@
+As the divers descend into the depths of the ocean, they are greeted by a surrealistic underwater city, bathed in the ethereal glow of bioluminescent creatures. The ancient structures rise like coral formations, adorned with intricate, mysterious symbols that seem to whisper forgotten tales. In this mesmerizing realm, the explorers uncover evidence of an advanced civilization lost to time. The image you create should capture the awe-inspiring beauty of this underwater metropolis, blending vibrant colors and dreamlike imagery. Let the surrealistic oceanic exploration art style bring to life the secrets and wonders that lie beneath the waves.

pieces/AudioTranscriptionPiece/models.py

@@ -1,4 +1,4 @@
-from pydantic import BaseModel, Field, FilePath
+from pydantic import BaseModel, Field
 from enum import Enum
 from typing import Union
 
@@ -16,7 +16,7 @@ class InputModel(BaseModel):
     """
     AudioTranscriptPiece input model
     """
-    audio_file_path: FilePath = Field(
+    audio_file_path: str = Field(
         ...,
         description='The path to the audio file to process.',
     )
@@ -43,7 +43,7 @@ class OutputModel(BaseModel):
         default=None,
         description="The result transcription text as a string."
     )
-    file_path_transcription_result: FilePath = Field(
+    file_path_transcription_result: str = Field(
         default=None,
         description="The result transcription text as a file path."
     )

pieces/AudioTranscriptionPiece/test_audio_transcription_piece.py

@@ -1,12 +1,10 @@
 import pytest
 from domino.testing import piece_dry_run
-from pathlib import PosixPath
-from pydantic import FilePath
 import os
 
 
 def run_piece(
-    audio_file_path: FilePath,
+    audio_file_path: str,
     output_type: str,
     temperature: float = 0.1,
 ):

pieces/ImageGeneratorPiece/models.py

@@ -1,4 +1,4 @@
-from pydantic import BaseModel, Field, FilePath
+from pydantic import BaseModel, Field
 from enum import Enum
 
 
@@ -59,7 +59,7 @@ class OutputModel(BaseModel):
         default=None,
         description="The generated image as string",
     )
-    output_file_path: FilePath = Field(
+    output_file_path: str = Field(
         default=None,
         description="Path to the generated image",
     )

pieces/InformationExtractionPiece/models.py

@@ -1,4 +1,4 @@
-from pydantic import BaseModel, Field, FilePath, Extra
+from pydantic import BaseModel, Field, Extra
 from enum import Enum
 from typing import List
 

pieces/PromptCreatorForImageGeneratorPiece/models.py

@@ -1,4 +1,4 @@
-from pydantic import BaseModel, Field, FilePath
+from pydantic import BaseModel, Field
 from enum import Enum
 
 
@@ -61,7 +61,7 @@ class OutputModel(BaseModel):
         default=None,
         description="The generated prompt to pass to an image generator AI",
     )
-    generated_prompt_file_path: FilePath = Field(
+    generated_prompt_file_path: str = Field(
         default=None,
         description="The path to the generated prompt, in .txt format",
     )

pieces/TextGeneratorPiece/models.py

@@ -1,4 +1,4 @@
-from pydantic import BaseModel, Field, FilePath
+from pydantic import BaseModel, Field
 from enum import Enum
 from typing import List
 
@@ -79,7 +79,7 @@ class OutputModel(BaseModel):
         default=None, 
         description="The generated text as a string"
     )
-    file_path_generated_text: FilePath = Field(
+    file_path_generated_text: str = Field(
         default=None, 
         description="The path to text file containing generated text"
     )

pieces/TextSummarizerPiece/models.py

@@ -1,4 +1,4 @@
-from pydantic import BaseModel, Field, FilePath
+from pydantic import BaseModel, Field
 from enum import Enum
 
 
@@ -31,7 +31,7 @@ class InputModel(BaseModel):
         default=None,
         description="Text to summarize",
     )
-    text_file_path: FilePath = Field(
+    text_file_path: str = Field(
         default=None,
         description="Use it only if not using text field. File path to the text to summarize"
     )
@@ -69,7 +69,7 @@ class OutputModel(BaseModel):
         default=None,
         description="summarized text"
     )
-    file_path_summarized_text: FilePath = Field(
+    file_path_summarized_text: str = Field(
         default=None,
         description="Path to summarized text file"
     )

pieces/TextSummarizerPiece/test_text_summarizer_piece.py

@@ -1,13 +1,12 @@
 from domino.testing import piece_dry_run
-from pydantic import FilePath
 import tiktoken
 import os
 
 def run_piece(
         output_type: str,
         completion_max_tokens: int,
         text: str = None,
-        text_file_path: FilePath = None,
+        text_file_path: str = None,
         chunk_size: int = 1000,
         chunk_overlap_rate: float = 0.2,
         openai_model: str = "gpt-3.5-turbo",