Update from Kaggle notebook

app.py

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+import gradio as gr
+
+def process_input(user_input):
+    """Process user input through the model and return the result."""
+    messages = [{"role": "user", "content": user_input}]
+    
+    # Apply chat template and generate response
+    input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
+    outputs = model.generate(input_tensor, max_new_tokens=300, pad_token_id=tokenizer.eos_token_id)
+    result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
+    
+    return result
+
+from peft import PeftModel
+
+output_weights_path = "/kaggle/working/fine_tuned_deepseek_math_weights.pth"
+torch.save(model.state_dict(), output_weights_path)
+
+import gradio as gr
+
+def process_input(user_input):
+    """Process user input through the model and return the result."""
+    messages = [{"role": "user", "content": user_input}]
+    
+    # Apply chat template and generate response
+    input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
+    outputs = model.generate(input_tensor, max_new_tokens=300, pad_token_id=tokenizer.eos_token_id)
+    result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
+    
+    return result
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=process_input,
+    inputs=gr.Textbox(placeholder="Enter your equation (e.g. 🥭 ÷ (🍋 - 🍊) = 2, 🍋 = 7, 🍊 = 3)"),
+    outputs=gr.Textbox(label="Model Output"),
+    title="Emoji Math Solver",
+    description="Enter a math equation with emojis, and the model will solve it."
+)
+
+demo.launch(share=True)
+
+import os
+from getpass import getpass
+from huggingface_hub import HfApi, Repository
+import re
+
+# Get your Hugging Face token
+hf_token = getpass("Enter your Hugging Face token: ")
+api = HfApi(token=hf_token)
+
+# Get your Space name (username/space-name)
+space_name = input("Enter your Hugging Face Space name (username/space-name): ")
+
+# Extract the Gradio code from your notebook
+# This assumes your Gradio app is defined in a cell or cells in your notebook
+from IPython import get_ipython
+
+# Get all cells from the notebook
+cells = get_ipython().user_ns.get('In', [])
+
+# Extract cells that contain Gradio code
+gradio_code = []
+in_gradio_block = False
+for cell in cells:
+    # Look for cells that import gradio or define the interface
+    if 'import gradio' in cell or 'gr.Interface' in cell or in_gradio_block:
+        in_gradio_block = True
+        gradio_code.append(cell)
+    # If we find a cell that seems to end the Gradio app definition
+    elif in_gradio_block and ('if __name__' in cell or 'demo.launch()' in cell):
+        gradio_code.append(cell)
+        in_gradio_block = False
+
+# Combine the code and ensure it has a launch method
+combined_code = "\n\n".join(gradio_code)
+
+# Make sure the app launches when run
+if 'if __name__ == "__main__"' not in combined_code:
+    combined_code += '\n\nif __name__ == "__main__":\n    demo.launch()'
+
+# Save to app.py
+with open("app.py", "w") as f:
+    f.write(combined_code)
+
+print("Extracted Gradio code and saved to app.py")
+
+# Clone the existing Space repository
+repo = Repository(
+    local_dir="space_repo",
+    clone_from=f"https://huggingface.co/spaces/{space_name}",
+    token=hf_token,
+    git_user="marwashahid",
+    git_email="marvashahid09@gmail.com"
+)
+
+# Copy app.py to the repository
+import shutil
+shutil.copy("app.py", "space_repo/app.py")
+
+# Add requirements if needed
+requirements = """
+gradio>=3.50.2
+"""
+with open("space_repo/requirements.txt", "w") as f:
+    f.write(requirements)
+
+# Commit and push changes
+repo.git_add()
+repo.git_commit("Update from Kaggle notebook")
+repo.git_push()
+
+print(f"Successfully deployed to https://huggingface.co/spaces/{space_name}")

requirements.txt

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+
+gradio>=3.50.2