Upload ghost_coder.py with huggingface_hub

ghost_coder.py

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+import pickle
+import sys
+import os  # <--- Added this to control Windows
+import math
+
+# ... (Keep the cosine_similarity_pure function as it is) ...
+def cosine_similarity_pure(vec1, matrix):
+    # (Keep your existing math logic here)
+    best_score = -1
+    best_index = -1
+    query_indices = vec1.indices
+    query_data = vec1.data
+    query_map = dict(zip(query_indices, query_data))
+    
+    num_rows = matrix.shape[0]
+    for i in range(num_rows):
+        row = matrix.getrow(i)
+        row_indices = row.indices
+        row_data = row.data
+        score = 0
+        for idx, val in zip(row_indices, row_data):
+            if idx in query_map:
+                score += val * query_map[idx]
+        if score > best_score:
+            best_score = score
+            best_index = i     
+    return best_index
+
+def load_brain():
+    try:
+        with open("ghost_brain.pkl", "rb") as f:
+            return pickle.load(f)
+    except FileNotFoundError:
+        print("❌ Error: 'ghost_brain.pkl' missing!")
+        input("Press Enter to exit...")
+        sys.exit()
+
+def main():
+    print("---------------------------------------")
+    print("   👻 GHOST CODEX (OFFLINE MODE) v1.1")
+    print("---------------------------------------")
+    
+    print("Loading Neural Indices...", end="")
+    vectorizer, tfidf_matrix, codes = load_brain()
+    print(" DONE.")
+    print("\n[READY] Type 'exit' to quit.")
+
+    while True:
+        user_input = input("\n>> ").strip()
+        if user_input.lower() in ["exit", "quit"]:
+            break
+        if not user_input: continue
+
+        try:
+            # Match
+            user_vec = vectorizer.transform([user_input])
+            try:
+                from sklearn.metrics.pairwise import cosine_similarity
+                best_idx = cosine_similarity(user_vec, tfidf_matrix).argmax()
+            except ImportError:
+                best_idx = cosine_similarity_pure(user_vec, tfidf_matrix)
+
+            result_code = codes[best_idx]
+            
+            # Print to terminal
+            print("\n[GENERATED SOLUTION]")
+            print("-" * 30)
+            print(result_code)
+            print("-" * 30)
+            
+            # Save AND Open
+            filename = "solution.txt"
+            with open(filename, "w", encoding="utf-8") as f:
+                f.write(result_code)
+            
+            print(f"   (Opening {filename} in Notepad...)")
+            os.startfile(filename)  # <--- THIS IS THE MAGIC LINE
+
+        except Exception as e:
+            print(f"Error: {e}")
+
+if __name__ == "__main__":
+    main()