Update app.py

app.py

@@ -440,6 +440,95 @@ REACT MODE
     
     return filepath
 
+def download_model_info():
+    """Create a text file with model download instructions."""
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    filename = f"model_download_instructions_{timestamp}.txt"
+    
+    content = """
+===============================================================================
+GPT-OSS-20B MODEL DOWNLOAD INSTRUCTIONS
+===============================================================================
+
+Model: openai/gpt-oss-20b
+Repository: https://huggingface.co/openai/gpt-oss-20b
+
+-------------------------------------------------------------------------------
+METHOD 1: Using Hugging Face CLI
+-------------------------------------------------------------------------------
+
+1. Install Hugging Face Hub:
+   pip install huggingface-hub
+
+2. Login to Hugging Face (optional but recommended):
+   huggingface-cli login
+
+3. Download the model:
+   huggingface-cli download openai/gpt-oss-20b
+
+-------------------------------------------------------------------------------
+METHOD 2: Using Python
+-------------------------------------------------------------------------------
+
+from huggingface_hub import snapshot_download
+
+model_path = snapshot_download(
+    repo_id="openai/gpt-oss-20b",
+    cache_dir="./models"
+)
+print(f"Model downloaded to: {model_path}")
+
+-------------------------------------------------------------------------------
+METHOD 3: Using Git LFS
+-------------------------------------------------------------------------------
+
+1. Install Git LFS:
+   git lfs install
+
+2. Clone the repository:
+   git clone https://huggingface.co/openai/gpt-oss-20b
+
+-------------------------------------------------------------------------------
+METHOD 4: Using transformers library
+-------------------------------------------------------------------------------
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("openai/gpt-oss-20b")
+tokenizer = AutoTokenizer.from_pretrained("openai/gpt-oss-20b")
+
+# Save locally
+model.save_pretrained("./local_model")
+tokenizer.save_pretrained("./local_model")
+
+-------------------------------------------------------------------------------
+SYSTEM REQUIREMENTS
+-------------------------------------------------------------------------------
+
+- Storage: ~40GB disk space
+- RAM: Minimum 16GB, recommended 32GB+
+- GPU: Optional but recommended (NVIDIA with CUDA support)
+- Python: 3.8 or higher
+
+-------------------------------------------------------------------------------
+USEFUL LINKS
+-------------------------------------------------------------------------------
+
+Model Card: https://huggingface.co/openai/gpt-oss-20b
+Documentation: https://huggingface.co/docs
+CLI Guide: https://huggingface.co/docs/huggingface_hub/guides/cli
+
+===============================================================================
+Generated: """ + datetime.now().strftime("%Y-%m-%d %H:%M:%S") + """
+===============================================================================
+"""
+    
+    filepath = f"/tmp/{filename}"
+    with open(filepath, 'w', encoding='utf-8') as f:
+        f.write(content)
+    
+    return filepath
+
 with gr.Blocks(title="LLM Reasoning Modes Comparison") as demo:
     gr.Markdown("""
     # LLM Reasoning Modes Comparison
@@ -468,6 +557,7 @@ with gr.Blocks(title="LLM Reasoning Modes Comparison") as demo:
             with gr.Row():
                 submit_btn = gr.Button("Run", variant="primary", size="lg")
                 download_btn = gr.Button("Download Results", variant="secondary", size="lg")
+                download_model_btn = gr.Button("Download Model Info", variant="secondary", size="lg")
         
         with gr.Column(scale=1):
             gr.Markdown("**Example Questions**")
@@ -487,7 +577,8 @@ with gr.Blocks(title="LLM Reasoning Modes Comparison") as demo:
         with gr.Column():
             react_output = gr.Markdown(label="ReAct Output")
     
-    download_file = gr.File(label="Download", visible=False)
+    download_file = gr.File(label="Download Results")
+    download_model_file = gr.File(label="Model Download Instructions")
     
     submit_btn.click(
         fn=run_comparison,
@@ -499,10 +590,12 @@ with gr.Blocks(title="LLM Reasoning Modes Comparison") as demo:
         fn=download_results,
         inputs=[think_output, act_output, react_output, question_input],
         outputs=download_file
-    ).then(
-        fn=lambda: gr.File(visible=True),
-        outputs=download_file
+    )
+    
+    download_model_btn.click(
+        fn=download_model_info,
+        outputs=download_model_file
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(share=True)