reshma-05 commited on
Commit
51eaf59
Β·
verified Β·
1 Parent(s): e3f7c53

Upload app.py

Browse files
Files changed (1) hide show
  1. app.py +17 -10
app.py CHANGED
@@ -3,22 +3,29 @@ import gradio as gr
3
  from transformers import AutoModelForCausalLM, AutoTokenizer
4
  import torch
5
 
6
- # Load IBM Granite model
7
- model_id = "ibm/granite-3.3-2b-instruct"
8
- token = os.getenv("HF_TOKEN") # Use Hugging Face Secrets
9
- tokenizer = AutoTokenizer.from_pretrained(model_id, use_auth_token=token)
10
- model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.float32, use_auth_token=token)
 
 
 
 
 
 
 
11
 
12
  def query_granite(prompt):
13
  inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
14
  outputs = model.generate(**inputs, max_new_tokens=100)
15
  return tokenizer.decode(outputs[0], skip_special_tokens=True)
16
 
17
- # Page functions
18
  def home():
19
  with gr.Blocks() as demo:
20
  gr.Markdown("# πŸ₯ Welcome to HealthAI")
21
- gr.Markdown("Your intelligent healthcare assistant with real AI support.")
22
  return demo
23
 
24
  def symptoms_app():
@@ -69,10 +76,10 @@ def faqs_app():
69
  with gr.Blocks() as demo:
70
  gr.Markdown("## ❓ FAQs")
71
  gr.Markdown("**Q1:** What is HealthAI?")
72
- gr.Markdown("**A:** It's an AI assistant to help with health-related queries using IBM Granite model.")
73
  return demo
74
 
75
- # Multi-page Gradio app
76
  pages = [
77
  gr.Page(title="🏠 Home", path="/", block=home),
78
  gr.Page(title="🩺 Symptoms", path="/symptoms", block=symptoms_app),
@@ -83,4 +90,4 @@ pages = [
83
  ]
84
 
85
  app = gr.App(pages=pages)
86
- app.launch()
 
3
  from transformers import AutoModelForCausalLM, AutoTokenizer
4
  import torch
5
 
6
+ # βœ… Correct IBM Granite model
7
+ model_id = "ibm-granite/granite-3.3-2b-instruct"
8
+ token = os.getenv("HF_TOKEN") # Load from Hugging Face Secrets
9
+
10
+ # βœ… Load model and tokenizer using the updated syntax
11
+ tokenizer = AutoTokenizer.from_pretrained(model_id, token=token)
12
+ model = AutoModelForCausalLM.from_pretrained(
13
+ model_id,
14
+ token=token,
15
+ device_map="auto",
16
+ torch_dtype=torch.float32
17
+ )
18
 
19
  def query_granite(prompt):
20
  inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
21
  outputs = model.generate(**inputs, max_new_tokens=100)
22
  return tokenizer.decode(outputs[0], skip_special_tokens=True)
23
 
24
+ # Pages
25
  def home():
26
  with gr.Blocks() as demo:
27
  gr.Markdown("# πŸ₯ Welcome to HealthAI")
28
+ gr.Markdown("Your intelligent healthcare assistant using IBM Granite 3.3-2B Instruct.")
29
  return demo
30
 
31
  def symptoms_app():
 
76
  with gr.Blocks() as demo:
77
  gr.Markdown("## ❓ FAQs")
78
  gr.Markdown("**Q1:** What is HealthAI?")
79
+ gr.Markdown("**A:** It's an AI assistant to help with health-related queries using IBM Granite 3.3-2B.")
80
  return demo
81
 
82
+ # App configuration
83
  pages = [
84
  gr.Page(title="🏠 Home", path="/", block=home),
85
  gr.Page(title="🩺 Symptoms", path="/symptoms", block=symptoms_app),
 
90
  ]
91
 
92
  app = gr.App(pages=pages)
93
+ app.launch()