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mirrored_repos
MachineLearning
meta-llama
Llama Recipes
Commits
34f6bd8d
Commit
34f6bd8d
authored
1 month ago
by
Sanyam Bhutani
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34f6bd8d
import
gradio
as
gr
import
pandas
as
pd
import
lancedb
from
lancedb.pydantic
import
LanceModel
,
Vector
from
lancedb.embeddings
import
get_registry
from
lancedb.rerankers
import
ColbertReranker
from
pathlib
import
Path
from
PIL
import
Image
import
io
import
base64
from
together
import
Together
import
os
import
logging
import
argparse
import
numpy
as
np
# Set up argument parsing
parser
=
argparse
.
ArgumentParser
(
description
=
"
Interactive Fashion Assistant
"
)
parser
.
add_argument
(
"
--images_folder
"
,
required
=
True
,
help
=
"
Path to the folder containing compressed images
"
)
parser
.
add_argument
(
"
--csv_path
"
,
required
=
True
,
help
=
"
Path to the CSV file with clothing data
"
)
parser
.
add_argument
(
"
--table_path
"
,
default
=
"
~/.lancedb
"
,
help
=
"
Table path for LanceDB
"
)
parser
.
add_argument
(
"
--use_existing_table
"
,
action
=
"
store_true
"
,
help
=
"
Use existing table if it exists
"
)
parser
.
add_argument
(
"
--api_key
"
,
required
=
True
,
help
=
"
Together API key
"
)
parser
.
add_argument
(
"
--default_model
"
,
default
=
"
BAAI/bge-large-en-v1.5
"
,
help
=
"
Default embedding model to use
"
)
args
=
parser
.
parse_args
()
# Set up logging
logging
.
basicConfig
(
level
=
logging
.
INFO
,
format
=
'
%(asctime)s - %(levelname)s - %(message)s
'
)
print
(
"
Starting the Fashion Assistant application...
"
)
# Define available models
AVAILABLE_MODELS
=
{
"
BAAI/bge-large-en-v1.5
"
:
"
bge-large-en-v1.5
"
,
"
BAAI/bge-small-en-v1.5
"
:
"
bge-small-en-v1.5
"
,
"
BAAI/bge-reranker-base
"
:
"
bge-reranker-base
"
,
"
BAAI/bge-reranker-large
"
:
"
bge-reranker-large
"
}
# Define retrieval methods
RETRIEVAL_METHODS
=
[
"
Semantic Search
"
,
"
Full Text Search
"
,
"
Hybrid Search
"
]
# Connect to LanceDB
print
(
"
Connecting to LanceDB...
"
)
db
=
lancedb
.
connect
(
args
.
table_path
)
def
create_table_for_model
(
model_name
):
print
(
f
"
Initializing the sentence transformer model:
{
model_name
}
"
)
model
=
get_registry
().
get
(
"
sentence-transformers
"
).
create
(
name
=
model_name
,
device
=
"
cpu
"
)
class
Schema
(
LanceModel
):
Filename
:
str
Title
:
str
Size
:
str
Gender
:
str
Description
:
str
=
model
.
SourceField
()
Category
:
str
Type
:
str
vector
:
Vector
(
model
.
ndims
())
=
model
.
VectorField
()
table_name
=
f
"
clothes_
{
AVAILABLE_MODELS
[
model_name
]
}
"
if
not
args
.
use_existing_table
:
tbl
=
db
.
create_table
(
name
=
table_name
,
schema
=
Schema
,
mode
=
"
overwrite
"
)
df
=
pd
.
read_csv
(
args
.
csv_path
)
df
=
df
.
dropna
().
astype
(
str
)
tbl
.
add
(
df
.
to_dict
(
'
records
'
))
tbl
.
create_fts_index
([
"
Description
"
],
replace
=
True
)
print
(
f
"
Created and populated table
{
table_name
}
"
)
else
:
tbl
=
db
.
open_table
(
table_name
)
tbl
.
create_fts_index
([
"
Description
"
],
replace
=
True
)
print
(
f
"
Opened existing table
{
table_name
}
"
)
return
tbl
tables
=
{
model
:
create_table_for_model
(
model
)
for
model
in
AVAILABLE_MODELS
}
current_table
=
tables
[
args
.
default_model
]
current_retrieval_method
=
"
Semantic Search
"
# Set up the Together API client
os
.
environ
[
"
TOGETHER_API_KEY
"
]
=
args
.
api_key
client
=
Together
(
api_key
=
args
.
api_key
)
print
(
"
Together API client set up successfully.
"
)
def
encode_image
(
image
):
buffered
=
io
.
BytesIO
()
image
.
save
(
buffered
,
format
=
"
JPEG
"
)
return
base64
.
b64encode
(
buffered
.
getvalue
()).
decode
(
'
utf-8
'
)
def
generate_description
(
image
):
print
(
"
Generating description for uploaded image...
"
)
base64_image
=
encode_image
(
image
)
try
:
response
=
client
.
chat
.
completions
.
create
(
model
=
"
meta-llama/Llama-Vision-Free
"
,
messages
=
[
{
"
role
"
:
"
user
"
,
"
content
"
:
[
{
"
type
"
:
"
image_url
"
,
"
image_url
"
:
{
"
url
"
:
f
"
data:image/jpeg;base64,
{
base64_image
}
"
}
},
{
"
type
"
:
"
text
"
,
"
text
"
:
"
Describe this clothing item in detail.
"
}
]
}
],
max_tokens
=
512
,
temperature
=
0.7
,
)
description
=
response
.
choices
[
0
].
message
.
content
print
(
f
"
Generated description:
{
description
}
"
)
return
description
except
Exception
as
e
:
print
(
f
"
Error generating description:
{
e
}
"
)
return
"
Error generating description
"
def
process_chat_input
(
chat_history
,
user_input
):
print
(
f
"
Processing chat input:
{
user_input
}
"
)
messages
=
[
{
"
role
"
:
"
system
"
,
"
content
"
:
"
You are a helpful fashion assistant.
"
}
]
for
user_msg
,
assistant_msg
in
chat_history
:
messages
.
append
({
"
role
"
:
"
user
"
,
"
content
"
:
user_msg
})
messages
.
append
({
"
role
"
:
"
assistant
"
,
"
content
"
:
assistant_msg
})
user_input
+=
"
. START YOUR MESSAGE DIRECTLY WITH A RESPONSE LIST. DO NOT REPEAT THE NAME OF THE ITEM MENTIONED IN THE QUERY. Start your message with
'
1. ..
'
"
messages
.
append
({
"
role
"
:
"
user
"
,
"
content
"
:
user_input
})
print
(
f
"
Chat history:
{
messages
}
"
)
try
:
bot_response
=
client
.
chat
.
completions
.
create
(
model
=
"
meta-llama/Llama-Vision-Free
"
,
messages
=
messages
,
max_tokens
=
512
,
temperature
=
0.7
,
).
choices
[
0
].
message
.
content
print
(
f
"
Bot response:
{
bot_response
}
"
)
return
user_input
,
bot_response
except
Exception
as
e
:
print
(
f
"
Error processing chat input:
{
e
}
"
)
return
user_input
,
"
Error processing chat input
"
def
retrieve_similar_items
(
description
,
n
=
10
):
print
(
f
"
Retrieving similar items for:
{
description
}
"
)
try
:
if
current_retrieval_method
==
"
Semantic Search
"
:
results
=
current_table
.
search
(
description
).
limit
(
n
).
to_pandas
()
elif
current_retrieval_method
==
"
Full Text Search
"
:
results
=
current_table
.
search
(
description
,
query_type
=
"
fts
"
).
limit
(
n
).
to_pandas
()
elif
current_retrieval_method
==
"
Hybrid Search
"
:
reranker
=
ColbertReranker
(
model_name
=
"
answerdotai/answerai-colbert-small-v1
"
,
column
=
"
Description
"
)
results
=
current_table
.
search
(
description
,
query_type
=
"
hybrid
"
).
rerank
(
reranker
=
reranker
).
limit
(
n
).
to_pandas
()
else
:
raise
ValueError
(
"
Invalid retrieval method
"
)
print
(
f
"
Retrieved
{
len
(
results
)
}
similar items using
{
current_retrieval_method
}
.
"
)
return
results
except
Exception
as
e
:
print
(
f
"
Error retrieving similar items:
{
e
}
"
)
return
pd
.
DataFrame
()
def
rewrite_query
(
original_query
,
item_description
):
print
(
f
"
Rewriting query:
{
original_query
}
"
)
messages
=
[
{
"
role
"
:
"
system
"
,
"
content
"
:
"
You are a helpful fashion assistant. Rewrite the user
'
s query to include details from the item description.
"
},
{
"
role
"
:
"
user
"
,
"
content
"
:
f
"
Item description:
{
item_description
}
"
},
{
"
role
"
:
"
user
"
,
"
content
"
:
f
"
User query:
{
original_query
}
"
},
{
"
role
"
:
"
user
"
,
"
content
"
:
"
Please rewrite the query to include relevant details from the item description.
"
}
]
try
:
response
=
client
.
chat
.
completions
.
create
(
model
=
"
meta-llama/Llama-Vision-Free
"
,
messages
=
messages
,
max_tokens
=
512
,
temperature
=
0.7
,
)
rewritten_query
=
response
.
choices
[
0
].
message
.
content
print
(
f
"
Rewritten query:
{
rewritten_query
}
"
)
return
rewritten_query
except
Exception
as
e
:
print
(
f
"
Error rewriting query:
{
e
}
"
)
return
original_query
def
fashion_assistant
(
image
,
chat_input
,
chat_history
):
if
chat_input
!=
""
:
print
(
"
Processing chat input...
"
)
last_description
=
chat_history
[
-
1
][
1
]
if
chat_history
else
""
user_message
,
bot_response
=
process_chat_input
(
chat_history
,
chat_input
)
similar_items
=
retrieve_similar_items
(
bot_response
)
gallery_data
=
create_gallery_data
(
similar_items
)
return
chat_history
+
[[
user_message
,
bot_response
]],
bot_response
,
gallery_data
,
last_description
elif
image
is
not
None
:
print
(
"
Processing uploaded image...
"
)
description
=
generate_description
(
image
)
user_message
=
f
"
I
'
ve uploaded an image. The description is:
{
description
}
"
user_message
,
bot_response
=
process_chat_input
(
chat_history
,
user_message
)
similar_items
=
retrieve_similar_items
(
description
)
gallery_data
=
create_gallery_data
(
similar_items
)
return
chat_history
+
[[
user_message
,
bot_response
]],
bot_response
,
gallery_data
,
description
else
:
print
(
"
No input provided.
"
)
return
chat_history
,
""
,
[],
""
def
create_gallery_data
(
results
):
return
[
(
str
(
Path
(
args
.
images_folder
)
/
row
[
'
Filename
'
]),
f
"
{
row
[
'
Title
'
]
}
\n
{
row
[
'
Description
'
]
}
"
)
for
_
,
row
in
results
.
iterrows
()
]
def
on_select
(
evt
:
gr
.
SelectData
):
return
f
"
Selected
{
evt
.
value
}
at index
{
evt
.
index
}
"
def
update_chat
(
image
,
chat_input
,
chat_history
,
last_description
):
new_chat_history
,
last_response
,
gallery_data
,
new_description
=
fashion_assistant
(
image
,
chat_input
,
chat_history
)
if
new_description
:
last_description
=
new_description
return
new_chat_history
,
new_chat_history
,
""
,
last_response
,
gallery_data
,
last_description
def
update_model
(
model_name
):
global
current_table
current_table
=
tables
[
model_name
]
return
f
"
Switched to model:
{
model_name
}
"
def
update_retrieval_method
(
method
):
global
current_retrieval_method
current_retrieval_method
=
method
return
f
"
Switched to retrieval method:
{
method
}
"
# Define the Gradio interface
print
(
"
Setting up Gradio interface...
"
)
with
gr
.
Blocks
()
as
demo
:
gr
.
Markdown
(
"
# Interactive Fashion Assistant
"
)
with
gr
.
Row
():
with
gr
.
Column
(
scale
=
1
):
image_input
=
gr
.
Image
(
type
=
"
pil
"
,
label
=
"
Upload Clothing Image
"
)
model_dropdown
=
gr
.
Dropdown
(
choices
=
list
(
AVAILABLE_MODELS
.
keys
()),
value
=
args
.
default_model
,
label
=
"
Embedding Model
"
)
retrieval_dropdown
=
gr
.
Dropdown
(
choices
=
RETRIEVAL_METHODS
,
value
=
"
Semantic Search
"
,
label
=
"
Retrieval Method
"
)
with
gr
.
Column
(
scale
=
1
):
chatbot
=
gr
.
Chatbot
(
label
=
"
Chat History
"
)
chat_input
=
gr
.
Textbox
(
label
=
"
Chat Input
"
)
chat_button
=
gr
.
Button
(
"
Send
"
)
with
gr
.
Column
(
scale
=
2
):
gallery
=
gr
.
Gallery
(
label
=
"
Retrieved Clothes
"
,
show_label
=
True
,
elem_id
=
"
gallery
"
,
columns
=
[
5
],
rows
=
[
2
],
object_fit
=
"
contain
"
,
height
=
"
auto
"
)
selected_image
=
gr
.
Textbox
(
label
=
"
Selected Image
"
)
chat_state
=
gr
.
State
([])
last_description
=
gr
.
State
(
""
)
image_input
.
change
(
update_chat
,
inputs
=
[
image_input
,
chat_input
,
chat_state
,
last_description
],
outputs
=
[
chat_state
,
chatbot
,
chat_input
,
chat_input
,
gallery
,
last_description
])
chat_button
.
click
(
update_chat
,
inputs
=
[
image_input
,
chat_input
,
chat_state
,
last_description
],
outputs
=
[
chat_state
,
chatbot
,
chat_input
,
chat_input
,
gallery
,
last_description
])
gallery
.
select
(
on_select
,
None
,
selected_image
)
model_dropdown
.
change
(
update_model
,
inputs
=
[
model_dropdown
],
outputs
=
[
gr
.
Textbox
(
label
=
"
Model Status
"
)])
retrieval_dropdown
.
change
(
update_retrieval_method
,
inputs
=
[
retrieval_dropdown
],
outputs
=
[
gr
.
Textbox
(
label
=
"
Retrieval Method Status
"
)])
# Disable embedding model dropdown when Hybrid Search is selected
retrieval_dropdown
.
change
(
lambda
x
:
gr
.
update
(
interactive
=
x
!=
"
Hybrid Search
"
),
inputs
=
[
retrieval_dropdown
],
outputs
=
[
model_dropdown
])
print
(
"
Gradio interface set up successfully. Launching the app...
"
)
demo
.
launch
()
print
(
"
Fashion Assistant application is now running!
"
)
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