Spaces:
Runtime error
Runtime error
adding final touches
Browse files- __pycache__/openai_manager.cpython-310.pyc +0 -0
- app.py +14 -12
__pycache__/openai_manager.cpython-310.pyc
CHANGED
Binary files a/__pycache__/openai_manager.cpython-310.pyc and b/__pycache__/openai_manager.cpython-310.pyc differ
|
|
app.py
CHANGED
@@ -2,6 +2,19 @@ import gradio as gr
|
|
2 |
from qdrant import qdrant_manager
|
3 |
from openai_manager import openai_manager
|
4 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
5 |
|
6 |
def generate(keywords):
|
7 |
try:
|
@@ -22,18 +35,7 @@ iface = gr.Interface(
|
|
22 |
fn=generate,
|
23 |
inputs="text",
|
24 |
outputs="text",
|
25 |
-
description="""
|
26 |
-
In this project, Im using Few-Shot Learning as an alternative to Fine-Tuning and Prompt
|
27 |
-
Engineering methods. While Prompt Engineering offers a cost-effective and swift approach
|
28 |
-
for development, it falls short in providing a comprehensive level of instruction
|
29 |
-
definition. For instance, crafting instructions that simulate a specific writing style proves to be exceptionally challenging.
|
30 |
-
On the other hand, Fine-Tuning excels in terms of instruction integration as it
|
31 |
-
comprehends and learns instructions rather than merely receiving them. However, it
|
32 |
-
comes with challenges such as complexity, high costs, and time-intensive processes.
|
33 |
-
Few-Shot Learning elegantly positions itself between these two approaches, offering the
|
34 |
-
best of both worlds. It provides an enticing balance that you might want to explore.
|
35 |
-
Why not give it a try?
|
36 |
-
""",
|
37 |
title="Sales Role Play Generator - Few Shots Learning",
|
|
|
38 |
)
|
39 |
iface.launch()
|
|
|
2 |
from qdrant import qdrant_manager
|
3 |
from openai_manager import openai_manager
|
4 |
|
5 |
+
description = """
|
6 |
+
In this project, Im using Few-Shot Learning as an alternative to Fine-Tuning and Prompt
|
7 |
+
Engineering methods. While Prompt Engineering offers a cost-effective and swift approach
|
8 |
+
for development, it falls short in providing a comprehensive level of instruction
|
9 |
+
definition. For instance, crafting instructions that simulate a specific writing style proves to be exceptionally challenging.
|
10 |
+
On the other hand, Fine-Tuning excels in terms of instruction integration as it
|
11 |
+
comprehends and learns instructions rather than merely receiving them. However, it
|
12 |
+
comes with challenges such as complexity, high costs, and time-intensive processes.
|
13 |
+
Few-Shot Learning elegantly positions itself between these two approaches, offering the
|
14 |
+
best of both worlds. It provides an enticing balance that you might want to explore.
|
15 |
+
Why not give it a try?
|
16 |
+
"""
|
17 |
+
|
18 |
|
19 |
def generate(keywords):
|
20 |
try:
|
|
|
35 |
fn=generate,
|
36 |
inputs="text",
|
37 |
outputs="text",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
38 |
title="Sales Role Play Generator - Few Shots Learning",
|
39 |
+
description=description,
|
40 |
)
|
41 |
iface.launch()
|