colpali fix

pages/Multimodal_Conversational_Search.py

@@ -208,11 +208,6 @@ def handle_input(state,dummy):
         if key.startswith('input_'):
             inputs[key.removeprefix('input_')] = st.session_state[key]
     st.session_state.inputs_ = inputs
-    
-    #######
-    
-    
-    #st.write(inputs) 
     question_with_id = {
         'question': inputs["query"],
         'id': len(st.session_state.questions_)
@@ -230,32 +225,7 @@ def handle_input(state,dummy):
         'image': out_['image'],
         'table':out_['table']
     })
-    #st.session_state.input_query=""
-    
-
-    
-# search_type = st.selectbox('Select the Search type',
-#     ('Conversational Search (RAG)',
-#     'OpenSearch vector search', 
-#     'LLM Text Generation'
-#     ),
-   
-#     key = 'input_searchType',
-#     help = "Select the type of retriever\n1. Conversational Search (Recommended) - This will include both the OpenSearch and LLM in the retrieval pipeline \n (note: This will put opensearch response as context to LLM to answer) \n2. OpenSearch vector search - This will put only OpenSearch's vector search in the pipeline, \n(Warning: this will lead to unformatted results )\n3. LLM Text Generation - This will include only LLM in the pipeline, \n(Warning: This will give hallucinated and out of context answers_)"
-#     )
-
-# col1, col2, col3, col4 = st.columns(4)
-    
-# with col1:
-#     st.text_input('Temperature', value = "0.001", placeholder='LLM Temperature', key = 'input_temperature',help = "Set the temperature of the Large Language model. \n Note: 1. Set this to values lower to 1 in the order of 0.001, 0.0001, such low values reduces hallucination and creativity in the LLM response; 2. This applies only when LLM is a part of the retriever pipeline")
-# with col2:
-#     st.number_input('Top K', value = 200, placeholder='Top K', key = 'input_topK', step = 50, help = "This limits the LLM's predictions to the top k most probable tokens at each step of generation, this applies only when LLM is a prt of the retriever pipeline")
-# with col3:
-#     st.number_input('Top P', value = 0.95, placeholder='Top P', key = 'input_topP', step = 0.05, help = "This sets a threshold probability and selects the top tokens whose cumulative probability exceeds the threshold while the tokens are generated by the LLM")
-# with col4:
-#     st.number_input('Max Output Tokens', value = 500, placeholder='Max Output Tokens', key = 'input_maxTokens', step = 100, help = "This decides the total number of tokens generated as the final response. Note: Values greater than 1000 takes longer response time")
-
-# st.markdown('---')
+    st.session_state.input_query=""
 
 
 def write_user_message(md):