Create app_1.py

file with transformer

app_1.py

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+import os
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from transformers import BitsAndBytesConfig
+import torch
+
+# ✅ Load the model and tokenizer
+MODEL_ID = "pareshmishra/mt564-gemma-lora"
+API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+if not API_TOKEN:
+    raise ValueError("HUGGINGFACEHUB_API_TOKEN environment variable not set")
+
+# Configure 4-bit quantization
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,  # Enable 4-bit quantization
+    bnb_4bit_compute_dtype=torch.float16,  # Use fp16 for computation
+    bnb_4bit_quant_type="nf4",  # Normal Float 4-bit quantization
+    bnb_4bit_use_double_quant=True  # Nested quantization for better efficiency
+)
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, token=API_TOKEN)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    token=API_TOKEN,
+    torch_dtype=torch.float16,  # fp16 as per model card
+    device_map="auto",  # Auto-map to GPU/CPU
+    quantization_config=quantization_config  # Use BitsAndBytesConfig
+)
+
+def respond(messages, chatbot_history, system_message, max_tokens, temperature, top_p):
+    try:
+        # Build prompt from history
+        prompt = f"{system_message.strip()}\n\n"
+        for msg in messages:
+            if isinstance(msg, dict):
+                role = msg.get("role")
+                content = msg.get("content", "")
+                if role == "user":
+                    prompt += f"User: {content.strip()}\n"
+                elif role == "assistant":
+                    prompt += f"Assistant: {content.strip()}\n"
+        prompt += "Assistant:"
+
+        # Tokenize and generate
+        inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=max_tokens,
+            temperature=temperature,
+            top_p=top_p,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id
+        )
+
+        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        response = response[len(prompt):].strip()
+
+        yield response if response else "⚠️ No response returned from the model."
+
+    except Exception as e:
+        yield f"❌ Error: {str(e)}\nDetails: {e.__class__.__name__}"
+
+# Gradio Interface
+demo = gr.ChatInterface(
+    fn=respond,
+    type="messages",
+    additional_inputs=[
+        gr.Textbox(
+            lines=3,
+            label="System message",
+            value="You are an expert in SWIFT MT564 financial messaging. Analyze, validate, and answer related user questions.",
+        ),
+        gr.Slider(50, 2048, value=512, step=1, label="Max new tokens"),
+        gr.Slider(0.1, 1.5, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-p sampling"),
+    ],
+    title="💬 MT564 Chat Assistant",
+    description="Analyze SWIFT MT564 messages or ask financial-related questions.",
+    theme="default"
+)
+
+if __name__ == "__main__":
+    demo.launch()