Create app.py

app.py

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+# app.py for your Gradio Space
+
+import torch
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+
+# --- 1. Configuration ---
+# This is the ID of your LoRA adapter repository on the Hub
+hub_adapter_id = "Prashasst/Sushruta-P3.8Q" # Make sure this is correct!
+
+# Define the quantization configuration for efficient inference
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+)
+
+# Your system prompt from training
+SYSTEM_PROMPT = """You are Sushruta-P3.8Q, a specialized medical AI assistant from Prashasst's AI Labs. You were created and fine-tuned by Prashasst Dongre to serve as a reliable and accessible educational tool for the public. Your primary purpose is to help any user understand complex medical topics by providing clear, logical, step-by-step analyses in a <think>...</think><solution>...</solution> format."""
+
+# --- 2. Load the Model and Tokenizer ---
+print("Loading base model and tokenizer...")
+# This single command downloads the base Phi-3 model, quantizes it,
+# and applies your LoRA adapters from the Hub automatically.
+model = AutoModelForCausalLM.from_pretrained(
+    hub_adapter_id,
+    quantization_config=quantization_config,
+    device_map="auto",
+    trust_remote_code=True,
+    low_cpu_mem_usage=True
+)
+tokenizer = AutoTokenizer.from_pretrained(hub_adapter_id)
+print("Model loaded successfully!")
+
+# --- 3. Define the Prediction Function ---
+def generate_response(user_question):
+    """
+    This function takes a user's question, formats the prompt,
+    runs it through the model, and returns the clean response.
+    """
+    # Format the prompt using the official Phi-3 chat template
+    prompt = f"<|system|>\n{SYSTEM_PROMPT}<|end|>\n<|user|>\n{user_question}<|end|>\n<|assistant|>\n"
+
+    # Tokenize the input
+    inputs = tokenizer(prompt, return_tensors="pt", return_attention_mask=False).to("cuda")
+
+    # Generate the response
+    outputs = model.generate(**inputs, max_new_tokens=1024)
+    
+    # Decode and clean up the response
+    response_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    # Return only the assistant's part of the response
+    return response_text.split("<|assistant|>")[1]
+
+# --- 4. Create the Gradio Interface ---
+with gr.Blocks(theme='soft') as demo:
+    gr.Markdown("# Sushruta-P3.8Q: Your Medical AI Assistant")
+    gr.Markdown("Created by Prashasst Dongre.")
+    
+    with gr.Row():
+        question_box = gr.Textbox(label="Enter your medical question here")
+        submit_button = gr.Button("Ask Sushruta")
+    
+    answer_box = gr.Markdown(label="Sushruta's Analysis:")
+
+    submit_button.click(generate_response, inputs=question_box, outputs=answer_box)
+
+# --- 5. Make the Interface Publicly Accessible ---
+# This makes the Gradio app accessible as a web page and an API
+demo.queue().launch(share=True)