Delete app.py

app.py

@@ -1,96 +0,0 @@
-import subprocess
-import sys
-import os
-
-# Install llama-cpp-python dependency
-def install_llama_cpp():
-    try:
-        print("Installing llama-cpp-python...")
-        subprocess.check_call([
-            sys.executable, '-m', 'pip', 'install', 
-            'llama-cpp-python', '--verbose'
-        ])
-        print("Successfully installed llama-cpp-python")
-        return True
-    except Exception as e:
-        print(f"Error installing llama-cpp-python: {e}")
-        return False
-
-# Install dependency first
-install_success = install_llama_cpp()
-
-if install_success:
-    try:
-        from llama_cpp import Llama
-        import gradio as gr
-        from huggingface_hub import hf_hub_download
-        
-        print("Downloading model from your repository...")
-        
-        # Download the model file from your repo with error handling
-        try:
-            model_path = hf_hub_download(
-                repo_id="Oromor/deepseek-coder-6.7b-instruct.Q4_0.gguf", 
-                filename="deepseek-coder-6.7b-instruct.Q4_0.gguf"
-            )
-            print(f"Model downloaded to: {model_path}")
-        except Exception as e:
-            print(f"Error downloading model: {e}")
-            raise
-        
-        # Load the model with error handling
-        try:
-            print("Loading Llama model...")
-            model = Llama(model_path=model_path, n_ctx=4096, verbose=False)
-            print("Model loaded successfully!")
-        except Exception as e:
-            print(f"Error loading model: {e}")
-            raise
-
-        def predict(input_text):
-            if not input_text.strip():
-                return "Please enter some text."
-            
-            try:
-                print(f"Generating response for: {input_text[:50]}...")
-                output = model(input_text, max_tokens=1500, temperature=0.7, top_p=0.9)
-                
-                if 'choices' in output and len(output['choices']) > 0:
-                    response = output['choices'][0]['text']
-                    return response.strip()
-                else:
-                    return f"Unexpected output format: {str(output)}"
-                    
-            except Exception as e:
-                return f"Error generating response: {str(e)}"
-
-        # Create Gradio interface
-        demo = gr.Interface(
-            fn=predict, 
-            inputs=gr.Textbox(label="Your message", placeholder="Ask me anything about coding..."),
-            outputs=gr.Textbox(label="Deepseek Coder Response"),
-            title="🤖 Deepseek Coder Chat",
-            description="Chat with Deepseek Coder 6.7B model"
-        )
-        
-        print("Starting Gradio interface...")
-        demo.launch()
-        
-    except ImportError as e:
-        import gradio as gr
-        
-        def error_predict(input_text):
-            return f"Failed to import required libraries: {str(e)}"
-        
-        demo = gr.Interface(fn=error_predict, inputs="text", outputs="text", title="Error")
-        demo.launch()
-        
-else:
-    # Fallback if installation fails
-    import gradio as gr
-    
-    def error_predict(input_text):
-        return "Failed to install llama-cpp-python. Please try again or contact support."
-    
-    demo = gr.Interface(fn=error_predict, inputs="text", outputs="text", title="Installation Error")
-    demo.launch()