Create app.py

app.py

@@ -0,0 +1,67 @@
+import os
+import gradio as gr
+import torch
+from train_get2_8_init import GPT, GPTConfig, generate_text, TrainingConfig
+from huggingface_hub import hf_hub_download
+
+def load_trained_model():
+    config = TrainingConfig()
+    model_config = GPTConfig(
+        block_size=config.block_size,
+        n_layer=config.n_layer,
+        n_head=config.n_head,
+        n_embd=config.n_embd,
+        dropout=config.dropout
+    )
+    
+    model = GPT(model_config)
+    model_path = hf_hub_download(
+        repo_id="padmanabhbosamia/Custom-Shakespeare",
+        filename="best_model.pt",
+        token=os.getenv('HF_TOKEN')  # Get token from environment variable
+    )
+    checkpoint = torch.load(model_path, map_location=config.device)
+    model.load_state_dict(checkpoint['model_state_dict'])
+    model.to(config.device)
+    model.eval()
+    return model
+
+def create_gradio_interface():
+    model = load_trained_model()
+    
+    def predict(prompt, max_length, temperature=0.7):
+        return generate_text(model, prompt, max_length, temperature)
+    
+    interface = gr.Interface(
+        fn=predict,
+        inputs=[
+            gr.Textbox(
+                lines=3,
+                label="Enter your prompt",
+                placeholder="Start typing here..."
+            ),
+            gr.Slider(
+                minimum=10,
+                maximum=500,
+                value=100,
+                step=10,
+                label="Maximum Length"
+            ),
+            gr.Slider(
+                minimum=0.1,
+                maximum=1.0,
+                value=0.7,
+                step=0.1,
+                label="Temperature (Higher = more creative)"
+            )
+        ],
+        outputs=gr.Textbox(lines=5, label="Generated Text"),
+        title="Custom GPT Text Generator (124M) based on Shakespeare",
+        description="A GPT-style language model trained on custom data by Shakespeare with 124M parameters"
+    )
+    return interface
+
+# For Hugging Face Spaces
+if __name__ == "__main__":
+    interface = create_gradio_interface()
+    interface.launch()