Working on frontend.

mi_clase.py → RAG.py

@@ -1,6 +1,6 @@
 # os.environ['CUDA_VISIBLE_DEVICES'] ='0'
 
-from my_dataset import datasetx
+from dataset_with_embeddings import datasetx
 from transformers import AutoModelForMultipleChoice
 from transformers import AutoTokenizer
 from nltk.corpus import stopwords

app.py

@@ -1,7 +1,10 @@
+import streamlit as st
+
 try:
-    import streamlit as st
-    from mi_clase import pipeline
-    st.title("Ask your scientific question!")
+
+    from RAG import pipeline
+
+    st.title("Ask your scientific question! 👨‍⚕")
     expected_format = "What is color?\nA)Is a name.\nB)Is something horrible.\nC)I don't know.\nD)You should ask someone else.\nE)Ask in a pyshic book."
     txt = st.text_area(
         "follow this format while making your question:",
@@ -28,12 +31,34 @@ try:
             "E":e
         }
 
+        multi = f'''
+        ---
+        :green[**Question**] 🔎
+
+        {mi_dict["prompt"]}
+
+        :green[**Options**] 📗
+
+        * :blue[**A**] {mi_dict["A"]}
+        * :blue[**B**] {mi_dict["B"]}
+        * :blue[**C**] {mi_dict["C"]}
+        * :blue[**D**] {mi_dict["D"]}
+        * :blue[**E**] {mi_dict["E"]}
+        ---
+        '''
+        st.markdown(multi)
+
         try:    
-            answer = pipeline.give_the_best_answer(mi_dict)
-            st.write(mi_dict)
-            st.write(answer)
+            
+
+
+            if st.button('Ask for answer'):
+                st.write('The answer is:')            
+                answer = pipeline.give_the_best_answer(mi_dict)
+                st.write(mi_dict)
+            #st.write(answer)
         except Exception as e:
-            st.error("something bad happend to the model")
+            st.error("Something bad happend while trying to infer the answer.")
             st.error(e)
 
     except Exception as e:
@@ -41,5 +66,5 @@ try:
         st.error(e)
 
 except Exception as e:
-    st.error("error at the beggining")
+    st.error("Error most likely related to the import of the object 'pipeline'")
     st.error(e)

my_dataset.py → dataset_with_embeddings.py

@@ -3,15 +3,21 @@ from datasets import load_from_disk, Dataset
 from huggingface_hub import hf_hub_download
 from datasets import load_dataset
 import faiss
-# load wikipedia dataset https://huggingface.co/docs/datasets/loading#hugging-face-hub
-datasetx = load_dataset("JosueElias/pipeline_dataset2")
+
 # load faiss file and get route of file https://huggingface.co/docs/huggingface_hub/guides/download#from-latest-version
 path2 = hf_hub_download(repo_id="JosueElias/pipeline_faiss", filename="faiss.index", repo_type="dataset")
+
+# load wikipedia dataset https://huggingface.co/docs/datasets/loading#hugging-face-hub
+datasetx = load_dataset("JosueElias/pipeline_dataset2")
+
 # save wikipedia dataset locally https://huggingface.co/docs/datasets/process#save
 datasetx.save_to_disk("./directory")
+
 # delete variable to have more memory space
 del datasetx
+
 # load dataset again in arrow format
 datasetx = load_from_disk("./directory/train")
+
 # load faiss to dataset
 datasetx.load_faiss_index('embeddings', path2)