Create app.py

app.py

@@ -0,0 +1,59 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
+import gradio as gr
+
+# --- Model Loading ---
+base_model_id = "meta-llama/Meta-Llama-3-8B"
+lora_model_id = "Nlpeva/lora_model"  # Replace with your LoRA Hub path
+
+try:
+    model = AutoModelForCausalLM.from_pretrained(
+        base_model_id,
+        torch_dtype=torch.float16,
+        device_map="auto"
+    )
+    tokenizer = AutoTokenizer.from_pretrained(base_model_id)
+    model = PeftModel.from_pretrained(model, lora_model_id)
+    print("Model and LoRA loaded successfully!")
+except Exception as e:
+    print(f"Error loading model or LoRA: {e}")
+    model = None
+    tokenizer = None
+
+# --- Generation Function ---
+def generate_response(information, input_text):
+    if model is None or tokenizer is None:
+        return "Model not loaded. Please check the logs."
+
+    prompt = f"Information: {information}\n\nInput: {input_text}\n\nResponse:"
+    input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.device)
+
+    try:
+        with torch.no_grad():
+            output = model.generate(
+                input_ids=input_ids,
+                max_length=300,  # Adjust as needed
+                num_return_sequences=1,
+                temperature=0.7,
+                top_p=0.9,
+                # Add other generation parameters as desired
+            )
+        generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+        return generated_text.strip()
+    except Exception as e:
+        return f"Error during generation: {e}"
+
+# --- Gradio Interface ---
+iface = gr.Interface(
+    fn=generate_response,
+    inputs=[
+        gr.Textbox(label="Information", placeholder="Provide any relevant context or information here."),
+        gr.Textbox(label="Input", placeholder="Enter your query or the text you want the model to process.")
+    ],
+    outputs=gr.Textbox(label="Output"),
+    title="Llama-3 with Custom LoRA",
+    description="Enter information and an input, and the model will generate a response based on both."
+)
+
+iface.launch()