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app.py

@@ -98,7 +98,7 @@ json_output_embedding = instructor_model.predict(
 
 json_file = open(json_output_embedding, "r")
 json_dict = json.load(json_file)
-dense_array = np.array(json_dict["data"][0], dtype=np.float64)
+dense_array = np.array(json_dict["data"], dtype=np.float64)
 dense_embedding = dense_array.tolist()
 
 text_embedding_instructions_choice = [
@@ -126,16 +126,16 @@ with col2:
             text_embedding_instructions_choice,
         )
 
-        pinecone_index_name = index_mapping[text_embedding_instruction]
-        pinecone.init(
-            api_key=st.secrets[f"pinecone_{pinecone_index_name}"],
-            environment="asia-southeast1-gcp-free",
-        )
-
-        pinecone_index = pinecone.Index(pinecone_index_name)
-
         submitted = st.form_submit_button("Submit")
         if submitted:
+            pinecone_index_name = index_mapping[text_embedding_instruction]
+            pinecone.init(
+                api_key=st.secrets[f"pinecone_{pinecone_index_name}"],
+                environment="asia-southeast1-gcp-free",
+            )
+
+            pinecone_index = pinecone.Index(pinecone_index_name)
+
             matches = query_pinecone(
                 dense_embedding, num_results, pinecone_index, indices
             )