Create lab/app.py

lab/app.py

@@ -0,0 +1,72 @@
+import gradio as gr
+import torch
+import torch.nn.functional as F
+from transformers import AutoTokenizer
+from unsloth import FastLanguageModel
+
+# Model Setup
+max_seq_length = 2048
+load_in_4bit = True
+name = "large-traversaal/Phi-4-Hindi"
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name=name,
+    max_seq_length=max_seq_length,
+    load_in_4bit=load_in_4bit,
+)
+
+model = FastLanguageModel.get_peft_model(
+    model,
+    r=16,
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
+    lora_alpha=16,
+    lora_dropout=0,
+    bias="none",
+    use_gradient_checkpointing="unsloth",
+    random_state=3407,
+    use_rslora=False,
+    loftq_config=None,
+)
+FastLanguageModel.for_inference(model)
+
+def generate_response(task, input_text, temperature, top_p, max_tokens):
+    prompt = f"### INPUT : {input_text} RESPONSE : "
+    message = [{"role": "user", "content": prompt}]
+    inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
+    
+    outputs = model.generate(
+        input_ids=inputs,
+        max_new_tokens=max_tokens,
+        use_cache=True,
+        temperature=temperature,
+        top_p=top_p,
+        pad_token_id=tokenizer.eos_token_id,
+    )
+    
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    processed_response = response.split("### RESPONSE :assistant")[-1].strip()
+    return processed_response
+
+# Gradio Interface
+def gradio_ui():
+    with gr.Blocks() as demo:
+        gr.Markdown("## Test Space: Chat with Phi-4-Hindi")
+        with gr.Row():
+            task = gr.Dropdown([
+                "Long Response", "Short Response", "NLI", "Translation", "MCQ", "Cross-Lingual"
+            ], label="Select Task")
+            input_text = gr.Textbox(label="Input Text")
+        
+        with gr.Row():
+            temperature = gr.Slider(0.1, 1.0, value=0.7, step=0.1, label="Temperature")
+            top_p = gr.Slider(0.1, 1.0, value=0.9, step=0.1, label="Top P")
+            max_tokens = gr.Slider(50, 800, value=200, step=50, label="Max Tokens")
+        
+        output_text = gr.Textbox(label="Generated Response")
+        btn = gr.Button("Generate")
+        btn.click(generate_response, inputs=[task, input_text, temperature, top_p, max_tokens], outputs=output_text)
+    
+    return demo
+
+# Launch Gradio App
+demo = gradio_ui()
+demo.launch()