Upload requirements.txt and app.py

app.py

@@ -0,0 +1,116 @@
+"""
+File: app.py
+Usage: Hugging Face Spaces Deployment
+Description: Academic submission for Domain-Specific Assistant via LLMs Fine-Tuning.
+"""
+
+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Hugging Face Hub repository containing the fine-tuned model
+MODEL_REPO = "degide/tinyllama-medical-assistant"
+
+print("Downloading and loading the fine-tuned medical chatbot...")
+
+# 1. Load the Tokenizer and Model directly from Hugging Face Hub
+tokenizer = AutoTokenizer.from_pretrained(MODEL_REPO, trust_remote_code=True)
+
+# Configuring the model for efficient CPU loading.
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_REPO,
+    device_map="cpu",
+    trust_remote_code=True,
+    torch_dtype=torch.float32,
+    low_cpu_mem_usage=True,
+)
+model.eval()
+
+print("Model loaded successfully!")
+
+def detect_ood(query):
+    """Heuristic-based Out-Of-Domain (OOD) detection."""
+    medical_keywords = [
+        'symptom', 'disease', 'treatment', 'medicine', 'doctor', 'health',
+        'diabetes', 'blood', 'pressure', 'heart', 'pain', 'sick', 'hospital',
+        'care', 'diagnosis', 'patient', 'clinic', 'drug', 'therapy', 'cancer',
+        'syndrome', 'infection', 'virus', 'bacteria', 'pill', 'dosage'
+    ]
+    
+    query_lower = query.lower()
+    has_medical = any(kw in query_lower for kw in medical_keywords)
+    
+    non_medical_patterns = [
+        'cook', 'recipe', 'weather', 'capital', 'python', 'code', 
+        'movie', 'song', 'game', 'sports', 'programming', 'math'
+    ]
+    is_non_medical = any(pattern in query_lower for pattern in non_medical_patterns)
+    
+    return is_non_medical or not has_medical
+
+def generate_medical_response(message, history):
+    """Generates the chatbot response with OOD handling."""
+    
+    if detect_ood(message):
+        return (
+            "**Out of Domain Detected:** I apologize, but I am a specialized medical "
+            "assistant and can only answer health-related questions. Could you please "
+            "ask me about medical symptoms, conditions, or treatments?\n\n"
+            "*Examples:*\n"
+            "- What are the symptoms of asthma?\n"
+            "- How is high blood pressure diagnosed?"
+        )
+    
+    prompt = f"<|system|>\nYou are a highly accurate and helpful medical assistant.</s>\n<|user|>\n{message}</s>\n<|assistant|>\n"
+    inputs = tokenizer(prompt, return_tensors="pt")
+    
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=256,
+            temperature=0.3, 
+            top_p=0.85,
+            repetition_penalty=1.1,
+            do_sample=True,
+            eos_token_id=tokenizer.eos_token_id,
+            pad_token_id=tokenizer.eos_token_id
+        )
+    
+    decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=False)
+    final_answer = decoded_output.split("<|assistant|>\n")[-1].replace("</s>", "").strip()
+    
+    disclaimer = (
+        "\n\n---\n"
+        "**Medical Disclaimer:** *This chatbot provides general health information "
+        "only based on fine-tuned data. It is not a replacement for professional "
+        "medical advice. Always consult a qualified healthcare provider.*"
+    )
+    
+    return final_answer + disclaimer
+
+# --- USER INTERFACE ---
+demo = gr.ChatInterface(
+    fn=generate_medical_response,
+    title="Domain-Specific Medical Assistant (TinyLlama)",
+    description=(
+        "An LLM fine-tuned via LoRA on the Medical Meadow Flashcards dataset. "
+        "Ask questions about medical symptoms, conditions, and treatments."
+    ),
+    examples=[
+        "What are the common symptoms of type 2 diabetes?",
+        "Explain the mechanism of action of metformin.",
+        "What is the prognosis for patients with stage 3 chronic kidney disease?",
+        "Describe the side effects of chemotherapy for breast cancer."
+    ],
+    chatbot=gr.Chatbot(height=600),
+    save_history=True,
+    fill_height=True,
+    fill_width=True,
+    submit_btn="Ask",
+    stop_btn="Stop"
+)
+
+if __name__ == "__main__":
+    demo.launch(
+        share=True,
+    )

requirements.txt

@@ -0,0 +1,10 @@
+torch
+transformers
+datasets
+gradio>=6.6.0
+accelerate
+bitsandbytes
+peft
+trl
+evaluate
+rouge_score