Create app.py

app.py

@@ -0,0 +1,87 @@
+import streamlit as st
+from unsloth import FastLanguageModel
+from transformers import AutoTokenizer
+import torch
+
+@st.cache_resource
+def load_model_and_tokenizer(model_name, hf_token):
+    # Load the model and tokenizer
+    model, tokenizer = FastLanguageModel.from_pretrained(
+        model_name=model_name,
+        max_seq_length=2048,
+        dtype=None,
+        load_in_4bit=True,
+        token=hf_token,
+    )
+    FastLanguageModel.for_inference(model)  # Enable optimized inference
+    return model, tokenizer
+
+def generate_solution(problem, model, tokenizer):
+    # Prepare the prompt using the same format as training
+    prompt_template = """Below is an instruction that describes a task, paired with an input that provides further context. 
+Write a response that appropriately completes the request. 
+Before answering, think carefully about the question and create a step-by-step chain of thoughts to ensure a logical and accurate response.
+
+### Instruction:
+You are a math expert. Please solve the following math problem. 
+
+### Problem:
+{}
+
+### Solution:
+<think>
+{{}}
+</think>
+{{}}"""
+
+    prompt = prompt_template.format(problem)
+
+    # Tokenize and prepare input
+    inputs = tokenizer(
+        [prompt], 
+        return_tensors="pt",
+        padding=True,
+    ).to("cuda")
+
+    # Generate solution
+    outputs = model.generate(
+        input_ids=inputs.input_ids,
+        attention_mask=inputs.attention_mask,
+        max_new_tokens=2000,
+        temperature=0.7,
+        pad_token_id=tokenizer.eos_token_id,
+        use_cache=True,
+    )
+
+    # Decode and format output
+    full_response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    # Extract the generated solution part
+    try:
+        solution = full_response.split("### Solution:")[1].strip()
+    except IndexError:
+        solution = full_response  # Fallback in case formatting fails
+    
+    return solution
+
+# Streamlit app
+st.title("Math Problem Solver")
+hf_token = st.text_input("Enter your Hugging Face token:")
+model_name = "shukdevdatta123/DeepSeek-R1-Math-Solutions"
+
+if hf_token:
+    # Load model and tokenizer
+    model, tokenizer = load_model_and_tokenizer(model_name, hf_token)
+
+    # Input for custom problem
+    custom_problem = st.text_input("Enter a math problem:")
+
+    if st.button("Generate Solution"):
+        if custom_problem:
+            solution = generate_solution(custom_problem, model, tokenizer)
+            st.write("### Generated Solution:")
+            st.write(solution)
+        else:
+            st.error("Please enter a math problem.")
+else:
+    st.warning("Please enter your Hugging Face token to load the model.")