screw forms!

app.py

@@ -66,36 +66,35 @@ st.markdown('You can use this tool to calculate different aspects of your model:
 
 st.markdown('### Dynamic Emissions')
 with st.expander("Calculate the emissions produced by energy consumption of model training"):
-    with st.form(key='dynamic_emissions'):
-        col1, col2, col3, col4 = st.columns(4)
-        with col1:
-            hardware = st.selectbox('GPU used', TDP['name'].tolist())
-            gpu_tdp = TDP['tdp_watts'][TDP['name'] == hardware].tolist()[0]
-            st.markdown("Different GPUs have different TDP (Thermal Design Power), which impacts how much energy you use.")
-        with col2:
-           training_time = st.number_input('Total number of GPU hours')
-           st.markdown('This is calculated by multiplying the number of GPUs you used by the training time: '
-                       'i.e. if you used 100 GPUs for 10 hours, this is equal to 100x10 = 1,000 GPU hours.')
-        with col3:
-           provider = st.selectbox('Provider used', providers)
-           st.markdown('If you can\'t find your provider here, select "Local/Private Infrastructure".')
-        with col4:
-            if provider != 'Local/Private Infastructure':
-                provider_instances = instances['region'][instances['provider'] == provider.lower()].unique().tolist()
-                region = st.selectbox('Provider used', provider_instances)
-                carbon_intensity = instances['impact'][(instances['provider'] == provider.lower()) & (instances['region'] == region)].tolist()[0]
-
-            else:
-                carbon_intensity = st.number_input('Carbon intensity of your energy grid, in grams of CO2 per kWh')
-                st.markdown('You can consult a resource like the [IEA](https://www.iea.org/countries) or '
-                            ' [Electricity Map](https://app.electricitymaps.com/) to get this information.')
-
-        dynamic_emissions = round(gpu_tdp * training_time * carbon_intensity/1000000)
-        st.metric(label="Dynamic emissions", value=str(dynamic_emissions)+' kilograms of CO2eq')
-        st.markdown('This is roughly equivalent to '+ str(round(dynamic_emissions/kg_per_mile,1)) + ' miles driven in an average US car'
-            ' produced in 2021. [(Source: energy.gov)](https://www.energy.gov/eere/vehicles/articles/fotw-1223-january-31-2022-average-carbon-dioxide-emissions-2021-model-year)')
-        st.form_submit_button(label="Anonymously share my data", help="Share the data from your model anonymously for research purposes!",\
-        on_click = lambda *args: write_to_csv(hardware, training_time, provider, carbon_intensity, dynamic_emissions))
+    col1, col2, col3, col4 = st.columns(4)
+    with col1:
+        hardware = st.selectbox('GPU used', TDP['name'].tolist())
+        gpu_tdp = TDP['tdp_watts'][TDP['name'] == hardware].tolist()[0]
+        st.markdown("Different GPUs have different TDP (Thermal Design Power), which impacts how much energy you use.")
+    with col2:
+       training_time = st.number_input('Total number of GPU hours')
+       st.markdown('This is calculated by multiplying the number of GPUs you used by the training time: '
+                   'i.e. if you used 100 GPUs for 10 hours, this is equal to 100x10 = 1,000 GPU hours.')
+    with col3:
+       provider = st.selectbox('Provider used', providers)
+       st.markdown('If you can\'t find your provider here, select "Local/Private Infrastructure".')
+    with col4:
+        if provider != 'Local/Private Infastructure':
+            provider_instances = instances['region'][instances['provider'] == provider.lower()].unique().tolist()
+            region = st.selectbox('Provider used', provider_instances)
+            carbon_intensity = instances['impact'][(instances['provider'] == provider.lower()) & (instances['region'] == region)].tolist()[0]
+
+        else:
+            carbon_intensity = st.number_input('Carbon intensity of your energy grid, in grams of CO2 per kWh')
+            st.markdown('You can consult a resource like the [IEA](https://www.iea.org/countries) or '
+                        ' [Electricity Map](https://app.electricitymaps.com/) to get this information.')
+    dynamic_emissions = round(gpu_tdp * training_time * carbon_intensity/1000000)
+    st.metric(label="Dynamic emissions", value=str(dynamic_emissions)+' kilograms of CO2eq')
+    st.markdown('This is roughly equivalent to '+ str(round(dynamic_emissions/kg_per_mile,1)) + ' miles driven in an average US car'
+    ' produced in 2021. [(Source: energy.gov)](https://www.energy.gov/eere/vehicles/articles/fotw-1223-january-31-2022-average-carbon-dioxide-emissions-2021-model-year)')
+    st.button(label="Anonymously share my data", help="Share the data from your model anonymously for research purposes!",\
+    on_click = lambda *args: write_to_csv(hardware, training_time, provider, carbon_intensity, dynamic_emissions))
+
 
 
 st.markdown('### Idle Emissions')