Update app.py

app.py

@@ -1,7 +1,55 @@
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+import torch
+from datasets import load_dataset
+from peft import AutoPeftModelForCausalLM
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+
+def format_instruction(report):
+    return """### Instruction: 
+Classify the student into Placed/NotPlaced based on his/her college report details. The report includes marks scored by the student in various courses and extra curricular activities taken by them.
+
+### Report:
+{report}
+
+### Label:
+"""
+
+def postprocess(outputs, tokenizer, prompt):
+    outputs = outputs.numpy()
+    outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+    output = outputs[0][len(prompt):]
+    
+    return output
+
+
+def run_model(report):
+    # load dataset and select a random sample
+    prompt = format_instruction(report)
+
+    # load base LLM model, LoRA params and tokenizer
+    model = AutoPeftModelForCausalLM.from_pretrained(
+        Model_Repo_ID,
+        low_cpu_mem_usage=True,
+        torch_dtype=torch.float16,
+        load_in_4bit=True,
+    )
+    tokenizer = AutoTokenizer.from_pretrained(Model_Repo_ID)
+    input_ids = tokenizer(prompt, return_tensors="pt", truncation=True).input_ids.cpu()
+    
+    # inference
+    with torch.inference_mode():
+        outputs = model.generate(
+            input_ids=input_ids, 
+            max_new_tokens=800, 
+            do_sample=True, 
+            top_p=0.9,
+            temperature=0.9
+        )
+
+    return postprocess(outputs, tokenizer, report)
+
+
+iface = gr.Interface(fn=run_model, students_report="text", Status="text")
 iface.launch()