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from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# 🔁 Replace with your actual Hugging Face model name
MODEL_NAME = "subhoshripal/smolified-context-bridge-slm"
# Load model + tokenizer
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
# ---------- FORMAT OUTPUT ----------
def format_output(text):
impact, business, action = "N/A", "N/A", "N/A"
text = text.lower()
# Try to extract sections flexibly
if "impact" in text:
try:
impact = text.split("impact")[1].split("\n")[0].replace("level", "").replace(":", "").strip().title()
except:
pass
if "business" in text:
try:
business = text.split("business")[1].split("strategic")[0].replace("translation", "").replace(":", "").strip().capitalize()
except:
pass
if "strategic" in text or "action" in text:
try:
action = text.split("strategic")[-1].replace("action", "").replace(":", "").strip().capitalize()
except:
pass
# FALLBACK: if parsing failed → use whole text smartly
if business == "N/A":
business = text[:120].capitalize()
if action == "N/A":
action = "Investigate the issue and apply appropriate fixes."
if impact == "N/A":
impact = "Moderate"
return impact, business, action
# ---------- MODEL FUNCTION ----------
def analyze(text):
if not text.strip():
return "N/A", "Please enter a system log.", "N/A"
prompt = f"""
You are an Industrial Systems Analyst.
Analyze the following technical log and respond STRICTLY in this format:
Impact Level (Low/Moderate/High/Critical):
Business Translation (1-2 lines, clear and concise):
Strategic Action (specific next step):
Log:
{text}
"""
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=100,
temperature=0.2, # more stable
top_p=0.9,
do_sample=True,
repetition_penalty=1.3
)
result = tokenizer.decode(
outputs[0][inputs["input_ids"].shape[-1]:],
skip_special_tokens=True
)
impact, business, action = format_output(result)
return impact, business, action
# ---------- UI ----------
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("""
# ContextBridge: From Logs to Decisions
### Translate technical errors into business insights in seconds
Paste a system log below
""")
input_box = gr.Textbox(
placeholder="e.g. API timeout after 3000ms while fetching user data",
label="System Log",
lines=3
)
gr.Examples(
examples=[
"API timeout after 3000ms while fetching user data",
"Unauthorized access attempt from IP 192.168.1.5",
"Database connection refused after multiple retries"
],
inputs=input_box
)
analyze_btn = gr.Button("Analyze ⚡")
impact_output = gr.Textbox(label="Impact Level")
business_output = gr.Textbox(label="Business Translation")
action_output = gr.Textbox(label="Strategic Action")
analyze_btn.click(
analyze,
inputs=input_box,
outputs=[impact_output, business_output, action_output]
)
demo.launch() |