Create app.py

app.py

@@ -0,0 +1,52 @@
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load the fine-tuned model and tokenizer
+def load_model_and_tokenizer(model_dir):
+    try:
+        tokenizer = AutoTokenizer.from_pretrained(model_dir)
+        model = AutoModelForCausalLM.from_pretrained(model_dir, torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32)
+        if torch.cuda.is_available():
+            model = model.to('cuda')
+        else:
+            model = model.to('cpu')
+    except Exception as e:
+        print(f"Error loading model: {e}")
+        return None, None
+    return model, tokenizer
+
+# Update the model directory path to use forward slashes
+model_dir = "KJX123/Llama2-7b-finetune"
+model, tokenizer = load_model_and_tokenizer(model_dir)
+
+# Function to generate code
+def generate_code(query):
+    if not model or not tokenizer:
+        return "Model or tokenizer not loaded properly."
+    
+    prompt = f"Query: {query}\nGitHub Code:\nYouTube Code:"
+    inputs = tokenizer(prompt, return_tensors='pt')
+    
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    inputs = inputs.to(device)
+    
+    outputs = model.generate(inputs['input_ids'], max_length=600, num_return_sequences=1)
+    generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return generated_code
+
+def gradio_interface(query):
+    code = generate_code(query)
+    return code
+
+# Define Gradio app layout
+iface = gr.Interface(
+    fn=gradio_interface,
+    inputs=gr.Textbox(lines=2, placeholder="Enter your query here..."),
+    outputs=gr.Textbox(lines=20, placeholder="Generated code will appear here..."),
+    title="Code Generator",
+    description="Enter a programming task or query to generate code using the fine-tuned model."
+)
+
+if __name__ == "__main__":
+    iface.launch()