Upload app.py with huggingface_hub

app.py

@@ -0,0 +1,142 @@
+import gradio as gr
+import json
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+# Lazy load model and tokenizer on first request (more reliable on Spaces)
+MODEL = None
+TOKENIZER = None
+
+def get_model():
+    global MODEL, TOKENIZER
+    if MODEL is None or TOKENIZER is None:
+        print("Loading model...")
+        base_model = AutoModelForCausalLM.from_pretrained(
+            "HuggingFaceTB/SmolLM-360M",
+            torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
+            device_map="auto"
+        )
+        tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM-360M")
+        if tokenizer.pad_token is None:
+            tokenizer.pad_token = tokenizer.eos_token
+
+        # Load LoRA adapters
+        model = PeftModel.from_pretrained(base_model, "waliaMuskaan011/calendar-event-extractor-smollm")
+        model.eval()
+        MODEL, TOKENIZER = model, tokenizer
+        print("Model loaded successfully!")
+    return MODEL, TOKENIZER
+
+def extract_calendar_event(event_text):
+    """Extract calendar information from natural language text."""
+    
+    if not event_text.strip():
+        return "Please enter some text describing a calendar event."
+    
+    model, tokenizer = get_model()
+    
+    # Build prompt
+    prompt = f"""Extract calendar fields from: "{event_text}".
+Return ONLY valid JSON with keys [action,date,time,attendees,location,duration,recurrence,notes].
+Use null for unknown.
+"""
+    
+    try:
+        # Tokenize and generate
+        inputs = tokenizer(prompt, return_tensors="pt")
+        inputs = {k: v.to(model.device) for k, v in inputs.items()}
+        
+        with torch.no_grad():
+            outputs = model.generate(
+                **inputs,
+                max_new_tokens=160,
+                temperature=0.0,
+                do_sample=False,
+                pad_token_id=tokenizer.eos_token_id
+            )
+        
+        # Decode response
+        full_response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        
+        # Extract JSON part (after the prompt)
+        response_start = full_response.find('"}') 
+        if response_start != -1:
+            json_part = full_response[response_start + 2:].strip()
+        else:
+            # Fallback: take everything after "Use null for unknown."
+            prompt_end = full_response.find("Use null for unknown.")
+            if prompt_end != -1:
+                json_part = full_response[prompt_end + len("Use null for unknown."):].strip()
+            else:
+                json_part = full_response.split("\n")[-1].strip()
+        
+        # Try to parse as JSON for validation
+        try:
+            parsed = json.loads(json_part)
+            return json.dumps(parsed, indent=2, ensure_ascii=False)
+        except json.JSONDecodeError:
+            return f"Generated (may need manual cleanup):\n{json_part}"
+            
+    except Exception as e:
+        return f"Error processing request: {str(e)}"
+
+# Create Gradio interface
+with gr.Blocks(title="Calendar Event Extractor", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 📅 Calendar Event Extractor
+    
+    This AI model extracts structured calendar information from natural language text.
+    Powered by fine-tuned SmolLM-360M with LoRA adapters.
+    
+    **Try it out**: Enter any calendar-related text and get structured JSON output!
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            input_text = gr.Textbox(
+                label="📝 Event Description",
+                placeholder="e.g., 'Meeting with John tomorrow at 2pm for 1 hour'",
+                lines=3
+            )
+            extract_btn = gr.Button("🔍 Extract Event Info", variant="primary")
+            
+        with gr.Column():
+            output_json = gr.Textbox(
+                label="📋 Extracted Information (JSON)",
+                lines=10,
+                max_lines=15
+            )
+    
+    # Examples
+    gr.Markdown("### 🔍 Try these examples:")
+    examples = gr.Examples(
+        examples=[
+            ["Quick meeting at the coworking space on 10th May 2025 starting at 11:00 am for 45 minutes"],
+            ["Coffee chat with Sarah tomorrow at 3pm"],
+            ["Weekly standup every Monday at 9am on Zoom"],
+            ["Doctor appointment next Friday at 2:30 PM for 30 minutes"],
+            ["Team lunch at the new restaurant on 15th December"],
+            ["Call with client on 25/12/2024 at 10:00 AM, needs to discuss project timeline"],
+        ],
+        inputs=[input_text],
+        outputs=[output_json],
+        fn=extract_calendar_event,
+        cache_examples=False
+    )
+    
+    extract_btn.click(
+        fn=extract_calendar_event,
+        inputs=[input_text],
+        outputs=[output_json]
+    )
+    
+    gr.Markdown("""
+    ---
+    **Model Details**: Fine-tuned SmolLM-360M using LoRA • **Dataset**: ~2500 calendar events • **Training**: Custom augmentation pipeline
+    
+    [🔗 Model Card](https://huggingface.co/waliaMuskaan011/calendar-event-extractor-smollm) • [💻 Training Code](https://github.com/muskaanwalia098/Calendar-Event-Entity-Extraction)
+    """)
+
+if __name__ == "__main__":
+    demo.launch()