Create app.py

app.py

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+import gradio as gr
+from huggingface_hub import hf_hub_download
+from llama_cpp import Llama
+
+# 1. Download the GGUF file from your Model repository
+print("Downloading/Locating model from Hugging Face...")
+model_path = hf_hub_download(
+    repo_id="iamabhayaditya/EfficientMath-AI",
+    filename="Meta-Llama-3.1-8B.Q4_K_M.gguf" 
+)
+
+# 2. Load the model using llama.cpp (Optimized for free CPU)
+print("Loading model into memory...")
+llm = Llama(
+    model_path=model_path,
+    n_ctx=2048,   
+    n_threads=4,  
+)
+
+# 3. Define the prediction function with a Polite Fallback
+def solve_math_problem(question):
+    try:
+        prompt = f"Below is a math word problem. Solve it step by step and provide the final answer.\n\n### Problem:\n{question}\n\n### Solution:\n"
+        
+        stream = llm(
+            prompt,
+            max_tokens=256,
+            temperature=0.2,
+            top_p=0.9,
+            stream=True,
+            stop=["<|end_of_text|>", "</s>", "<|eot_id|>"] 
+        )
+        
+        generated_text = ""
+        for output in stream:
+            generated_text += output["choices"][0]["text"]
+            yield generated_text
+            
+    except Exception as e:
+        # Prints the actual technical error to your server logs for debugging
+        print(f"Server Error: {str(e)}")
+        # Yields a safe, friendly message to the end-user
+        yield "Oops! I encountered a slight issue calculating that problem. Could you please try again or rephrase the question?"
+
+# 4. Build the Black & Orange Custom UI
+custom_css = """
+.gradio-container { background-color: #000000 !important; }
+.markdown-text h1 { color: #ff7f00 !important; }
+.markdown-text p { color: #cccccc !important; }
+textarea {
+    border: 2px solid #ff7f00 !important;
+    background-color: #111111 !important;
+    color: #ffffff !important;
+}
+button.primary {
+    background: linear-gradient(90deg, #ff7f00, #ffaa00) !important;
+    border: none !important;
+    color: black !important;
+    font-weight: bold !important;
+}
+span.svelte-1gfkn6j, .label { color: #ff7f00 !important; }
+"""
+
+with gr.Blocks(theme=gr.themes.Monochrome(), css=custom_css) as app:
+    gr.Markdown("<h1 style='text-align: center; margin-top: 20px;'>EfficientMath-AI</h1>")
+    gr.Markdown("<p style='text-align: center;'>This is a custom fine-tuned Llama 3.1 8B model, trained to solve grade school math word problems.</p>")
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            user_input = gr.Textbox(lines=5, placeholder="Enter a math word problem here...", label="Question")
+            
+            gr.Examples(
+                examples=[
+                    "A bag containing 30 apples weighs 6 kg. How much will 1080 apples weigh?",
+                    "If the cost of 18 apples is 90 rupees, what is the cost of 24 apples?",
+                    "Abhay has 16 apples, he borrowed 5 from Akash then gave 14 to Shivam. How many apples is Abhay left with?"
+                ],
+                inputs=user_input,
+                label="Click an example below to test:"
+            )
+            
+            with gr.Row():
+                clear_btn = gr.ClearButton([user_input])
+                submit_btn = gr.Button("Submit", variant="primary")
+                
+        with gr.Column(scale=1):
+            model_output = gr.Textbox(label="Model Solution", lines=5, max_lines=50, interactive=False)
+            
+    submit_btn.click(fn=solve_math_problem, inputs=user_input, outputs=model_output)
+
+# Launch natively in Hugging Face (no debug mode, no share link needed)
+app.launch()