Update README.md

README.md

@@ -1,62 +1,27 @@
 ---
+title: Infrnce Private Api
+emoji: 👁
+colorFrom: green
+colorTo: indigo
+sdk: gradio
+sdk_version: 5.38.0
+app_file: app.py
+pinned: false
 license: apache-2.0
-base_model: distilbert-base-uncased
-tags:
-- text-classification
-- log-analysis
-- openstack
-- distilbert
-- fine-tuned
-datasets:
-- custom
-language:
-- en
-pipeline_tag: text-classification
----
-
-# INFRNCE BERT Log Classification Model
-
-This is a fine-tuned DistilBERT model for classifying OpenStack Nova log entries into different operational categories.
+short_description: infrnce-backend
 
-## Model Details
 
-- **Base Model**: distilbert-base-uncased
-- **Task**: Multi-class text classification
-- **Number of Labels**: 6
-- **Domain**: OpenStack log analysis
 
-## Labels
 
-The model classifies logs into the following categories:
 
-- Error_Handling, - Instance_Management, - Network_Operations, - Resource_Management, - Scheduler_Operations, - System_Operations
 
-## Usage
 
-```python
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import torch
 
-# Load the model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained("your-username/infrnce-bert-log-classifier")
-model = AutoModelForSequenceClassification.from_pretrained("your-username/infrnce-bert-log-classifier")
 
-# Example usage
-log_text = "Your OpenStack log entry here"
-inputs = tokenizer(log_text, return_tensors="pt", truncation=True, padding=True, max_length=512)
 
-with torch.no_grad():
-    outputs = model(**inputs)
-    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
-    predicted_class_id = predictions.argmax().item()
 
-print(f"Predicted class: {model.config.id2label[predicted_class_id]}")
-```
-
-## Training Data
+---
 
-The model was trained on a curated dataset of OpenStack Nova logs with both regex-based classifications and semantic clustering.
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
-## Performance
 
-The model was trained with controlled accuracy to achieve optimal performance on log classification tasks.