Update app.py
Browse files
app.py
CHANGED
@@ -8,8 +8,9 @@ import spaces
|
|
8 |
import markdown
|
9 |
import requests
|
10 |
import torch
|
|
|
11 |
from PIL import Image
|
12 |
-
from transformers import MllamaForConditionalGeneration, AutoProcessor
|
13 |
|
14 |
|
15 |
device = 'cuda' if torch.cuda.is_available() else 'cpu'
|
@@ -27,6 +28,11 @@ model = MllamaForConditionalGeneration.from_pretrained(
|
|
27 |
processor = AutoProcessor.from_pretrained(model_id)
|
28 |
|
29 |
|
|
|
|
|
|
|
|
|
|
|
30 |
SYSTEM_INSTRUCTION="You are DailySnap, your job is to anlyse the given image and provide daily journal about the image and use some random time"
|
31 |
|
32 |
def extract_assistant_reply(input_string):
|
@@ -84,6 +90,28 @@ def generate__image_desc(image):
|
|
84 |
html_output = markdown.markdown(markdown_text)
|
85 |
return html_output
|
86 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
87 |
# Define activity categories based on detected objects
|
88 |
activity_categories = {
|
89 |
"Working": ["laptop", "computer", "keyboard", "office chair"],
|
|
|
8 |
import markdown
|
9 |
import requests
|
10 |
import torch
|
11 |
+
import io
|
12 |
from PIL import Image
|
13 |
+
from transformers import MllamaForConditionalGeneration, AutoProcessor,AutoModelForCausalLM, AutoTokenizer
|
14 |
|
15 |
|
16 |
device = 'cuda' if torch.cuda.is_available() else 'cpu'
|
|
|
28 |
processor = AutoProcessor.from_pretrained(model_id)
|
29 |
|
30 |
|
31 |
+
model_name = "Qwen/Qwen2.5-Coder-7B-Instruct"
|
32 |
+
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
|
33 |
+
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
34 |
+
|
35 |
+
|
36 |
SYSTEM_INSTRUCTION="You are DailySnap, your job is to anlyse the given image and provide daily journal about the image and use some random time"
|
37 |
|
38 |
def extract_assistant_reply(input_string):
|
|
|
90 |
html_output = markdown.markdown(markdown_text)
|
91 |
return html_output
|
92 |
|
93 |
+
@spaces.GPU
|
94 |
+
def generate_journal_infographics(code_input):
|
95 |
+
prompt = f"Generate daily journal inforgraphics using html for the following:\n\n{code_input}"
|
96 |
+
|
97 |
+
messages = [
|
98 |
+
{"role": "system", "content": "You are DailySnap, a highly efficient and intelligent assistant designed to generate infographics using htmnl bootstrap icon and generate highly appealing daily journal as per the user detail"},
|
99 |
+
{"role": "user", "content": prompt}
|
100 |
+
]
|
101 |
+
|
102 |
+
# Prepare inputs for the model
|
103 |
+
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
|
104 |
+
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
|
105 |
+
|
106 |
+
# Generate the documentation
|
107 |
+
generated_ids = model.generate(**model_inputs, max_new_tokens=4000)
|
108 |
+
generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]
|
109 |
+
documentation = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
|
110 |
+
print(documentation)
|
111 |
+
return documentation
|
112 |
+
|
113 |
+
|
114 |
+
|
115 |
# Define activity categories based on detected objects
|
116 |
activity_categories = {
|
117 |
"Working": ["laptop", "computer", "keyboard", "office chair"],
|