Update app.py

app.py

@@ -1,48 +1,49 @@
-import torch
 import requests
-from transformers import XLNetTokenizer
+import torch
+from transformers import XLNetTokenizer, XLNetForSequenceClassification
 import gradio as gr
 
-# Link to the saved model on Hugging Face Spaces
-model_link = 'https://huggingface.co/spaces/AliArshad/SeverityPrediction/blob/main/severitypredictor.pt'
+# URL of the saved model on GitHub
+model_url = 'https://github.com/AliArshadswl/severity_prediction/raw/main/XLNet_model_project_Core.pt'
 
-# Download the model file
-response = requests.get(model_link)
-model_path = 'severitypredictor.pt'
-with open(model_path, 'wb') as f:
-    f.write(response.content)
+# Function to download the model from URL and load it
+def download_model(url):
+    response = requests.get(url)
+    with open('XLNet_model_project_Core.pt', 'wb') as f:
+        f.write(response.content)
 
-# Try loading the downloaded file as a PyTorch model
-try:
-    model = torch.load(model_path)
-    tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')
+# Download the model
+download_model(model_url)
 
-    # Function for prediction
-    def xl_net_predict(text):
-        inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=100)
-        with torch.no_grad():
-            outputs = model(**inputs)
-        logits = outputs.logits
-        probabilities = torch.softmax(logits, dim=1)
-        predicted_class = torch.argmax(probabilities).item()
-        return "Severe" if predicted_class == 1 else "Non-severe"
+# Load the saved model
+tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')
+model = XLNetForSequenceClassification.from_pretrained('xlnet-base-cased', num_labels=2)
+model.load_state_dict(torch.load('XLNet_model_project_Core.pt', map_location=torch.device('cpu')))
+model.eval()
 
-    # Customizing the interface
-    iface = gr.Interface(
-        fn=xl_net_predict,
-        inputs=gr.Textbox(lines=2, label="Summary", placeholder="Enter text here..."),
-        outputs=gr.Textbox(label="Predicted Severity"),
-        title="XLNet Based Bug Report Severity Prediction",
-        description="Enter text and predict its severity (Severe or Non-severe).",
-        theme="huggingface",
-        examples=[
-            ["Can't open multiple bookmarks at once from the bookmarks sidebar using the context menu"],
-            ["Minor enhancements to make-source-package.sh"]
-        ],
-        allow_flagging=False
-    )
+# Function for prediction
+def xl_net_predict(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=100)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    probabilities = torch.softmax(logits, dim=1)
+    predicted_class = torch.argmax(probabilities).item()
+    return "Severe" if predicted_class == 1 else "Non-severe"
 
-    iface.launch()
+# Customizing the interface
+iface = gr.Interface(
+    fn=xl_net_predict,
+    inputs=gr.Textbox(lines=2, label="Summary", placeholder="Enter text here..."),
+    outputs=gr.Textbox(label="Predicted Severity"),
+    title="XLNet Based Bug Report Severity Prediction",
+    description="Enter text and predict its severity (Severe or Non-severe).",
+    theme="huggingface",
+    examples=[
+        ["Can't open multiple bookmarks at once from the bookmarks sidebar using the context menu"],
+        ["Minor enhancements to make-source-package.sh"]
+    ],
+    allow_flagging=False
+)
 
-except Exception as e:
-    print(f"An error occurred: {e}")
+iface.launch()