Create app.py

app.py

@@ -0,0 +1,77 @@
+import streamlit as st
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
+from peft import PeftModel
+
+def main():
+    st.title("Math Meme Repair (LoRA-Fine-Tuned)")
+
+    st.markdown("""
+    **Instructions**:
+    1. Enter your incorrect math meme in the format:
+       ```
+       Math Meme Correction:
+       Incorrect: 5-3-1 = 3?
+       Correct:
+       ```
+    2. Click **Repair Math Meme** to generate a corrected explanation.
+
+    **Note**: This is running on CPU, so it may be slow and memory-intensive for a 7B model.
+    """)
+
+    # 1. Load the base model from Hugging Face
+    model_name = "deepseek-ai/deepseek-math-7b-base"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    
+    # If your CPU doesn't support float16, switch to float32.
+    # (float16 might not work well on certain CPUs)
+    base_model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        torch_dtype=torch.float32  # CPU-friendly dtype
+    )
+    base_model = base_model.to("cpu")  # We'll run on CPU
+
+    # 2. Load your LoRA adapter (local directory with adapter_config.json & adapter_model.safetensors)
+    adapter_dir = "trained-math-meme-model"
+    model = PeftModel.from_pretrained(base_model, adapter_dir)
+    model = model.to("cpu")
+
+    # 3. Configure generation
+    generation_config = GenerationConfig(
+        max_new_tokens=100,
+        temperature=0.7,
+        top_p=0.7,
+        pad_token_id=tokenizer.eos_token_id
+    )
+
+    # 4. User input area
+    user_input = st.text_area(
+        "Enter your math meme input:",
+        value="Math Meme Correction:\nIncorrect: 5-3-1 = 3?\nCorrect:"
+    )
+
+    if st.button("Repair Math Meme"):
+        if user_input.strip() == "":
+            st.warning("Please enter a math meme input following the required format.")
+        else:
+            with torch.no_grad():
+                # Tokenize on CPU
+                encoding = tokenizer(user_input, return_tensors="pt").to("cpu")
+                outputs = model.generate(
+                    input_ids=encoding.input_ids,
+                    attention_mask=encoding.attention_mask,
+                    max_new_tokens=generation_config.max_new_tokens,
+                    temperature=generation_config.temperature,
+                    top_p=generation_config.top_p,
+                    pad_token_id=generation_config.pad_token_id
+                )
+
+            # Decode and display
+            result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+            st.subheader("Repaired Math Meme")
+            st.write(result)
+
+            st.markdown("\n**Error Rating:** 90% sass, 10% patience (on CPU)")
+
+if __name__ == "__main__":
+    main()