Update app.py

app.py

@@ -686,9 +686,6 @@ def task_categorization_model_predictions():
     
     
 def model_selector_sbert():
-    
-    st.image("./panelup.png")
-
     # st.title("Text Analysis App")
 
     data = "" 
@@ -697,6 +694,7 @@ def model_selector_sbert():
 
     st.write("""Enter a brief description of your task, and this app will recommend an AI model for you!""")
     
+    st.image("./panelup.png")
     # st.markdown(f'<p style="color: #012d51;font-size:18px;border-radius:%;">{"Please, describe your AI application below:"}</p>', unsafe_allow_html=True)
 
     prompt = st.text_area("Describe your task:")
@@ -704,51 +702,52 @@ def model_selector_sbert():
     st.write("")
     st.write("")
 
-    if prompt != "":
-        sbert_saved_model = torch.load("Sbert_saved_model", map_location=torch.device('cpu')).to("cpu")
-        model = sbert_saved_model.to("cpu")
-        tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-mpnet-base-v2")
-        pipe = TextClassificationPipeline(model= model, tokenizer=tokenizer, return_all_scores=True)
-        # outputs a list of dicts like [[{'label': 'NEGATIVE', 'score': 0.0001223755971295759},  {'label': 'POSITIVE', 'score': 0.9998776316642761}]]
-
-        # prompt = ["What is the the best ai for putting text report into data table?","How can I generate car sales agreement with ai model?","AI model to detect burglar on 48 hours of cctv video footage","I need Ai model help me with rewriting 50 financial statements emails into one summary report ?","I need a model for extracting person from an image"]
-        # responses = pipe(prompt)
-
-
-        models_list = pd.read_csv("models.csv")
-    #     st.write(get_top_3(prompt))
-
-        top_cat, top_models = get_top_3_a(prompt,pipe)
-        # prompt = input("Enter your AI task idea:")
-    #     top_cats,cat_to_models = get_models(prompt)
-
-    #     top_models = cat_to_models[top_cats[0]]
-
-        top_cat = "  " + top_cat[0].upper() + top_cat[1:]
-        st.markdown(f'<p style="color: #012d51;font-size:24px;border-radius:%;">{"Recognized AI Domain Category: "}</p>', unsafe_allow_html=True)
-
-        st.markdown(f'<p style="background-color:#12d51; color:#1782ea;font-size:18px;border-radius:%;">{top_cat}</p>', unsafe_allow_html=True)
-    #     st.write("Recommended AI Domain Type: ",top_cat)
-    #     st.write("Recommended Models for category: ",top_cats[0], " are:",top_models)
-
-    #     st.write("Recommended Task category: ",top_models[0])
-
-
-        st.markdown(f'<p style=" margin-left: 0px;color: #012d51;font-size:18px;border-radius:%;">{"The best models selected for this task:"}</p>', unsafe_allow_html=True)
-
-
-        st.markdown(f'<p style="margin-left: 0px;background-color:#e1e1e1; color:#012d51;font-size:18px;border-radius:%;">{"1- "+top_models[0]}</p>', unsafe_allow_html=True)
-
-        st.image("./buttons1.png")
-
-        if st.button("Show more"): 
-
-            st.markdown(f'<p style="margin-left: 0px;background-color:#e1e1e1; color:#012d51;font-size:18px;border-radius:%;">{"2- "+top_models[1]}</p>', unsafe_allow_html=True)
-            st.image("./buttons1.png")
-
-
-            st.markdown(f'<p style="margin-left: 0px;background-color:#e1e1e1; color:#012d51;font-size:18px;border-radius:%;">{"3- "+top_models[2]}</p>', unsafe_allow_html=True)
+    if st.button("Recommend Model"):
+        if prompt != "":
+            sbert_saved_model = torch.load("Sbert_saved_model", map_location=torch.device('cpu')).to("cpu")
+            model = sbert_saved_model.to("cpu")
+            tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-mpnet-base-v2")
+            pipe = TextClassificationPipeline(model= model, tokenizer=tokenizer, return_all_scores=True)
+            # outputs a list of dicts like [[{'label': 'NEGATIVE', 'score': 0.0001223755971295759},  {'label': 'POSITIVE', 'score': 0.9998776316642761}]]
+    
+            # prompt = ["What is the the best ai for putting text report into data table?","How can I generate car sales agreement with ai model?","AI model to detect burglar on 48 hours of cctv video footage","I need Ai model help me with rewriting 50 financial statements emails into one summary report ?","I need a model for extracting person from an image"]
+            # responses = pipe(prompt)
+    
+    
+            models_list = pd.read_csv("models.csv")
+        #     st.write(get_top_3(prompt))
+    
+            top_cat, top_models = get_top_3_a(prompt,pipe)
+            # prompt = input("Enter your AI task idea:")
+        #     top_cats,cat_to_models = get_models(prompt)
+    
+        #     top_models = cat_to_models[top_cats[0]]
+    
+            top_cat = "  " + top_cat[0].upper() + top_cat[1:]
+            st.markdown(f'<p style="color: #012d51;font-size:24px;border-radius:%;">{"Recognized AI Domain Category: "}</p>', unsafe_allow_html=True)
+    
+            st.markdown(f'<p style="background-color:#12d51; color:#1782ea;font-size:18px;border-radius:%;">{top_cat}</p>', unsafe_allow_html=True)
+        #     st.write("Recommended AI Domain Type: ",top_cat)
+        #     st.write("Recommended Models for category: ",top_cats[0], " are:",top_models)
+    
+        #     st.write("Recommended Task category: ",top_models[0])
+    
+    
+            st.markdown(f'<p style=" margin-left: 0px;color: #012d51;font-size:18px;border-radius:%;">{"The best models selected for this task:"}</p>', unsafe_allow_html=True)
+    
+    
+            st.markdown(f'<p style="margin-left: 0px;background-color:#e1e1e1; color:#012d51;font-size:18px;border-radius:%;">{"1- "+top_models[0]}</p>', unsafe_allow_html=True)
+    
             st.image("./buttons1.png")
+    
+            if st.button("Show more"): 
+    
+                st.markdown(f'<p style="margin-left: 0px;background-color:#e1e1e1; color:#012d51;font-size:18px;border-radius:%;">{"2- "+top_models[1]}</p>', unsafe_allow_html=True)
+                st.image("./buttons1.png")
+    
+    
+                st.markdown(f'<p style="margin-left: 0px;background-color:#e1e1e1; color:#012d51;font-size:18px;border-radius:%;">{"3- "+top_models[2]}</p>', unsafe_allow_html=True)
+                st.image("./buttons1.png")