Update app.py

app.py

@@ -41,17 +41,6 @@ index.add(embeddings)
 # Load BERT model for encoding search queries
 model = SentenceTransformer('all-mpnet-base-v2')
 
-def exact_text_match(query, metadata):
-    matches = []
-    query_lower = query.lower()
-    for patent_number, data in metadata.items():
-        text = data.get('text', '')
-        if isinstance(text, float):
-            text = ''
-        if query_lower in text.lower():
-            matches.append((patent_number, 1.0))  # Score of 1.0 for exact match
-    return matches
-
 def search(query, top_k=5):
     print(f"Searching for: {query}")
     
@@ -79,23 +68,7 @@ def search(query, top_k=5):
         result += f"Similarity Score: {distances[0][i]:.4f}\n\n"
         results.append(result)
     
-    # Fallback to exact text match if no results or low similarity
-    if not results or distances[0][0] < 0.5:
-        print("Falling back to exact text match")
-        exact_matches = exact_text_match(query, metadata)
-        if exact_matches:
-            print(f"Exact matches found: {exact_matches}")
-        else:
-            print("No exact matches found")
-        for patent_number, score in exact_matches[:top_k]:
-            patent_data = metadata[patent_number]
-            result = f"Patent Number: {patent_number}\n"
-            text = patent_data.get('text', 'No text available')
-            result += f"Text: {text[:200]}...\n"
-            result += f"Exact Match Score: {score:.4f}\n\n"
-            results.append(result)
-    
-    return "\n".join(results)
+    return "\n".join(results[:top_k])
 
 # Create Gradio interface
 iface = gr.Interface(