Update app.py

app.py

@@ -4,7 +4,7 @@ from fastapi import FastAPI
 app = FastAPI(docs_url="/")
 
 @app.get("/search={query}&similarity={similarity}")
-def search(query, similarity=False):
+def search(query, similarity="false"):
     import time
     import requests
 
@@ -12,7 +12,7 @@ def search(query, similarity=False):
 
     # Set the API endpoint and query parameters
     url = "https://www.googleapis.com/books/v1/volumes"
-    params = {"q": str(query), "printType": "books", "maxResults": 1}
+    params = {"q": str(query), "printType": "books", "maxResults": 30}
 
     # Send a GET request to the API with the specified parameters
     response = requests.get(url, params=params)
@@ -64,7 +64,7 @@ def search(query, similarity=False):
     pyalex.config.email = "ber2mir@gmail.com"
 
     # Define a pager object with the same query
-    pager = Works().search(str(query)).paginate(per_page=1, n_max=1)
+    pager = Works().search(str(query)).paginate(per_page=10, n_max=10)
 
     # Generate a list of the results
     openalex_results = list(pager)
@@ -111,7 +111,7 @@ def search(query, similarity=False):
             },
             {
                 "role": "user",
-                "content": f"Recommend me 1 books about {query}. Your response should be like: 'title: <title>, author: <author>, publisher: <publisher>, summary: <summary>'",
+                "content": f"Recommend me 10 books about {query}. Your response should be like: 'title: <title>, author: <author>, publisher: <publisher>, summary: <summary>'",
             },
         ],
     )
@@ -175,7 +175,7 @@ def search(query, similarity=False):
 
     # Combine title, description, and publisher into a single string
     combined_data = [
-        f"{title} {description} {publisher}"
+        f"The book's title is {title.title()}. It is published by {publisher.title()}. This book is about {description}"
         for title, description, publisher in zip(titles, descriptions, publishers)
     ]
 
@@ -240,14 +240,14 @@ def search(query, similarity=False):
     ]
 
     # Get the predicted labels
-    classes = [zs_classifier(doc, candidate_labels) for doc in combined_data]
+    classes = zs_classifier(combined_data, candidate_labels)
 
     # Calculate the elapsed time
     end_time = time.time()
     runtime = f"{end_time - start_time:.2f} seconds"
 
     # Calculate the similarity between the books
-    if similarity:
+    if similarity != "false":
         from sentence_transformers import util
 
         sentence_transformer = SentenceTransformer("all-MiniLM-L6-v2")
@@ -268,6 +268,8 @@ def search(query, similarity=False):
                     "sorted_by_similarity": similarity_sorted[0][1:],
                 }
             )
+    else:
+        similar_books = [{"sorted_by_similarity": []} for i in range(len(titles))]
 
     # Create a list of dictionaries to store the results
     results = []