Update app.py

app.py

@@ -157,104 +157,105 @@ async def run_function_on_text(top_k,study_obj,study_type,phase,purpose,allocati
       """
     )
 
-  # LLM.complete
-  complete_response  = await llm.acomplete(f"""
-      Based on the provided instructions and clinical trial information, generate the new eligibility criteria by analyzing clinical trial information(### Clinical Trial Information).
-      ### Instruction:
-      Criteria generation:
-      As a clinical researcher, generate new eligibility criteria for given clinical trial information.
-      Ensure the criteria are clear, specific, and reasonable for a clinical research information.
-
-      Prioritize the following topics in clinical trial information.:
-      1. Study Objectives
-      2. Study Design and Phases
-      3. Conditions
-      4. Intervention/Treatment
-
-      Please follow the pattern of the output(### Pattern of the output).
-      --------------------------------------------------
-      ### Clinical Trial Information
-      {study_information}
-      --------------------------------------------------
-      ### Pattern of the output
-      Inclusion Criteria
-      1.
-      2.
-      .
-      .
-      .
-
-      Exclusion Criteria
-      1.
-      2.
-      .
-      .
-      .
-
-
-      """
+  # # LLM.complete
+  # complete_response  = await llm.acomplete(f"""
+  #     Based on the provided instructions and clinical trial information, generate the new eligibility criteria by analyzing clinical trial information(### Clinical Trial Information).
+  #     ### Instruction:
+  #     Criteria generation:
+  #     As a clinical researcher, generate new eligibility criteria for given clinical trial information.
+  #     Ensure the criteria are clear, specific, and reasonable for a clinical research information.
+
+  #     Prioritize the following topics in clinical trial information.:
+  #     1. Study Objectives
+  #     2. Study Design and Phases
+  #     3. Conditions
+  #     4. Intervention/Treatment
+
+  #     Please follow the pattern of the output(### Pattern of the output).
+  #     --------------------------------------------------
+  #     ### Clinical Trial Information
+  #     {study_information}
+  #     --------------------------------------------------
+  #     ### Pattern of the output
+  #     Inclusion Criteria
+  #     1.
+  #     2.
+  #     .
+  #     .
+  #     .
+
+  #     Exclusion Criteria
+  #     1.
+  #     2.
+  #     .
+  #     .
+  #     .
+
+
+  #     """
+  #   )
+
+  # combine_response = await llm.acomplete(f"""
+  #     Based on the provided instructions clinical, clinical trial information, and criteria information, generate the appropriate eligibility criteria for ### Clinical Trial Information by analyze clinical trial information(### Clinical Trial Information), criteria 1 (### Criteria 1) and criteria 2 (### Criteria 2).
+  #     ### Instruction:
+  #     Criteria generation:
+  #     As a clinical researcher, generate appropriate eligibility criteria by analyzing given information.
+  #     Ensure the criteria are clear, specific, and reasonable for a clinical research information(### Clinical Trial Information).
+
+  #     Prioritize the following topics in clinical trial information.:
+  #     1. Study Objectives
+  #     2. Study Design and Phases
+  #     3. Conditions
+  #     4. Intervention/Treatment
+
+  #     Do not generate redundant inclusion and exclusion criteria. For example, if a criterion is included in one set of inclusion or exclusion criteria, do not include it again.
+
+  #     Reference Papers generation:
+  #     Please give us NCT IDs and study names from the references list in ### Criteria 1.
+
+  #     Please follow the pattern of the output(### Pattern of the output).
+  #     --------------------------------------------------
+  #     ### Clinical Trial Information
+  #     {study_information}
+  #     --------------------------------------------------
+  #     ### Criteria 1
+  #     {query_response}
+  #     --------------------------------------------------
+  #     ### Criteria 2
+  #     {complete_response}
+  #     --------------------------------------------------
+  #     ### Pattern of the output
+  #     Inclusion Criteria
+  #     1.
+  #     2.
+  #     .
+  #     .
+  #     .
+
+  #     Exclusion Criteria
+  #     1.
+  #     2.
+  #     .
+  #     .
+  #     .
+
+  #     Reference Papers
+  #     1.NCT ID:
+  #       Study Name:
+  #       Condition:
+  #       Intervention/Treatment:
+  #     2.NCT ID:
+  #       Study Name:
+  #       Condition:
+  #       Intervention/Treatment:
+  #     .
+  #     .
+  #     .
+  #     """
     )
 
-  combine_response = await llm.acomplete(f"""
-      Based on the provided instructions clinical, clinical trial information, and criteria information, generate the appropriate eligibility criteria for ### Clinical Trial Information by analyze clinical trial information(### Clinical Trial Information), criteria 1 (### Criteria 1) and criteria 2 (### Criteria 2).
-      ### Instruction:
-      Criteria generation:
-      As a clinical researcher, generate appropriate eligibility criteria by analyzing given information.
-      Ensure the criteria are clear, specific, and reasonable for a clinical research information(### Clinical Trial Information).
-
-      Prioritize the following topics in clinical trial information.:
-      1. Study Objectives
-      2. Study Design and Phases
-      3. Conditions
-      4. Intervention/Treatment
-
-      Do not generate redundant inclusion and exclusion criteria. For example, if a criterion is included in one set of inclusion or exclusion criteria, do not include it again.
-
-      Reference Papers generation:
-      Please give us NCT IDs and study names from the references list in ### Criteria 1.
-
-      Please follow the pattern of the output(### Pattern of the output).
-      --------------------------------------------------
-      ### Clinical Trial Information
-      {study_information}
-      --------------------------------------------------
-      ### Criteria 1
-      {query_response}
-      --------------------------------------------------
-      ### Criteria 2
-      {complete_response}
-      --------------------------------------------------
-      ### Pattern of the output
-      Inclusion Criteria
-      1.
-      2.
-      .
-      .
-      .
-
-      Exclusion Criteria
-      1.
-      2.
-      .
-      .
-      .
-
-      Reference Papers
-      1.NCT ID:
-        Study Name:
-        Condition:
-        Intervention/Treatment:
-      2.NCT ID:
-        Study Name:
-        Condition:
-        Intervention/Treatment:
-      .
-      .
-      .
-      """
-    )
-
-  return query_response,complete_response,combine_response
+  return query_response
+  # return query_response,complete_response,combine_response
 
 # Place holder
 place_holder = f"""Study Objectives
@@ -406,19 +407,20 @@ with gr.Blocks() as demo:
               label="Response 1",
               lines=15,
               interactive=False)
-      with gr.Column():
-          rag_box = gr.Textbox(
-              label="Response 2",
-              lines=15,
-              interactive=False)
-      with gr.Column():
-          combine_box = gr.Textbox(
-              label="Response 3",
-              lines=15,
-              interactive=False)
+      # with gr.Column():
+      #     rag_box = gr.Textbox(
+      #         label="Response 2",
+      #         lines=15,
+      #         interactive=False)
+      # with gr.Column():
+      #     combine_box = gr.Textbox(
+      #         label="Response 3",
+      #         lines=15,
+      #         interactive=False)
           
   inputs_information = [top_k_box, study_obj_box, study_type_box, phase_box, purpose_box, allocation_box, intervention_model_box, masking_box, conditions_box, intervention_box, location_box, removed_location_box]
-  outputs_information = [base_box, rag_box,combine_box]
+  outputs_information = [base_box]
+  # outputs_information = [base_box, rag_box,combine_box]
 
   submit_button.click(
       run_function_on_text,
@@ -428,26 +430,26 @@ with gr.Blocks() as demo:
 
   clear_button.click(lambda : [None] * len(inputs_information), outputs=inputs_information)
 
-  with gr.Row():
-      selected_response = gr.Radio(
-          choices=[
-              "Response 1",
-              "Response 2",
-              "Response 3",
-              "All responses are equally good",
-              "Neither response is satisfactory"
-              ],
-          label="Select the best response"
-      )
-  with gr.Row():
-      flag_button = gr.Button("Flag Selected Response")
-
-  #Flagging
-  dataset_name = "ravistech/feedback-demo-space"
-  hf_writer = gr.HuggingFaceDatasetSaver(hf_token=token_w, dataset_name=dataset_name, private=True)
-  hf_writer.setup([selected_response, study_obj_box, study_type_box, phase_box, purpose_box, allocation_box, intervention_model_box, masking_box, conditions_box, intervention_box, location_box, removed_location_box, top_k_box, base_box, rag_box, combine_box],dataset_name)
-
-  flag_button.click(lambda *args: hf_writer.flag(list(args)), [selected_response, study_obj_box, study_type_box, phase_box, purpose_box, allocation_box, intervention_model_box, masking_box, conditions_box, intervention_box, location_box, removed_location_box, top_k_box, base_box, rag_box, combine_box], None, preprocess=False)
+  # with gr.Row():
+  #     selected_response = gr.Radio(
+  #         choices=[
+  #             "Response 1",
+  #             "Response 2",
+              # "Response 3",
+  #             "All responses are equally good",
+  #             "Neither response is satisfactory"
+  #             ],
+  #         label="Select the best response"
+  #     )
+  # with gr.Row():
+  #     flag_button = gr.Button("Flag Selected Response")
+
+  # #Flagging
+  # dataset_name = "ravistech/feedback-demo-space"
+  # hf_writer = gr.HuggingFaceDatasetSaver(hf_token=token_w, dataset_name=dataset_name, private=True)
+  # hf_writer.setup([selected_response, study_obj_box, study_type_box, phase_box, purpose_box, allocation_box, intervention_model_box, masking_box, conditions_box, intervention_box, location_box, removed_location_box, top_k_box, base_box, rag_box, combine_box],dataset_name)
+
+  # flag_button.click(lambda *args: hf_writer.flag(list(args)), [selected_response, study_obj_box, study_type_box, phase_box, purpose_box, allocation_box, intervention_model_box, masking_box, conditions_box, intervention_box, location_box, removed_location_box, top_k_box, base_box, rag_box, combine_box], None, preprocess=False)
 
   #Clear all
   with gr.Row():