Create geo_engine.py

geo_engine.py

@@ -0,0 +1,54 @@
+from scoring.corpus import corpus_score, corpus_recommendation
+from scoring.structure import structure_score, structure_recommendation
+from scoring.quality import quality_score, quality_recommendation
+from scoring.hygiene import hygiene_score, hygiene_recommendation
+
+def compute_full_geo(input_text: str) -> dict:
+    """
+    Combine all four scoring modules. Compute:
+      - corpus_score     (0–100)
+      - structure_score  (0–100)
+      - quality_score    (0–100)
+      - hygiene_score    (0–100)
+      - geo_score        = average of all four
+      - recommendations: dict with four strings
+    """
+    # 1. Corpus Presence & Authority
+    c_score = corpus_score(input_text)
+    c_rec   = corpus_recommendation(input_text, c_score)
+
+    # 2. Extractability & Structure
+    s_score = structure_score(input_text)
+    s_rec   = structure_recommendation(input_text, s_score)
+
+    # 3. Content Quality & E-E-A-T
+    q_score = quality_score(input_text)
+    q_rec   = quality_recommendation(input_text, q_score)
+
+    # 4. Technical AI-Crawl Hygiene
+    h_score = hygiene_score(input_text)
+    h_rec   = hygiene_recommendation(input_text, h_score)
+
+    # Weighted GEO Score (equal 25% each)
+    geo_score = int(
+        0.25 * c_score +
+        0.25 * s_score +
+        0.25 * q_score +
+        0.25 * h_score
+    )
+
+    recommendations = {
+        "corpus":    c_rec,
+        "structure": s_rec,
+        "quality":   q_rec,
+        "hygiene":   h_rec
+    }
+
+    return {
+        "corpus_score":    c_score,
+        "structure_score": s_score,
+        "quality_score":   q_score,
+        "hygiene_score":   h_score,
+        "geo_score":       geo_score,
+        "recommendations": recommendations
+    }