Create app.py

app.py

@@ -0,0 +1,37 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import os
+
+# Get Hugging Face token from environment variable
+HF_TOKEN = os.getenv("HF_TOKEN")
+if not HF_TOKEN:
+    raise ValueError("Please set HF_TOKEN environment variable with your Hugging Face access token")
+
+# Load model and tokenizer
+model_name = "iimran/AnalyserV1"
+tokenizer = AutoTokenizer.from_pretrained(model_name, token=HF_TOKEN)
+model = AutoModelForSequenceClassification.from_pretrained(model_name, token=HF_TOKEN)
+
+def classify_complaint(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=256)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    return model.config.id2label[torch.argmax(outputs.logits).item()]
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=classify_complaint,
+    inputs=gr.Textbox(lines=3, placeholder="Enter your complaint here...", label="Complaint Text"),
+    outputs=gr.Label(label="Predicted Category"),
+    title="Complaint Category Classifier",
+    description="Automatically classify community complaints into specific categories",
+    examples=[
+        ["I wanted to bring to your attention that a huge big truck has been parked on Main Street"],
+        ["There are overgrown bushes on Oak Road that pose a fire risk"],
+        ["Excessive noise from construction site during night hours"]
+    ]
+)
+
+if __name__ == "__main__":
+    demo.launch()