Update app.py

app.py

@@ -314,186 +314,278 @@ interventions_place_holder = f"""Example:
 
 prefilled_value = f"""Study Objectives The purpose of this study is to find out if the combination of docetaxel and capecitabine can shrink the size of breast tumors and preserve the breast. Conditions: Breast Cancer Intervention / Treatment: DRUG: Docetaxel, DRUG: Capecitabine Location: United States Study Design and Phases Study Type: INTERVENTIONAL Phase: PHASE2 Primary Purpose: TREATMENT Allocation: RANDOMIZED Interventional Model: PARALLEL Masking: NONE"""
 
-# custom_css = """
-# .gradio-container {
-#     font-family: 'Roboto', sans-serif;
-# }
-# .main-header {
-#     text-align: center;
-#     color: #4a4a4a;
-#     margin-bottom: 2rem;
-# }
-# .tab-header {
-#     font-size: 1.2rem;
-#     font-weight: bold;
-#     margin-bottom: 1rem;
-# }
-# .custom-chatbot {
-#     border-radius: 10px;
-#     box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
-# }
-# .custom-button {
-#     background-color: #3498db;
-#     color: white;
-#     border: none;
-#     padding: 10px 20px;
-#     border-radius: 5px;
-#     cursor: pointer;
-#     transition: background-color 0.3s ease;
-# }
-# .custom-button:hover {
-#     background-color: #2980b9;
-# }
-# """
-
-# # Define Gradio theme
-# theme = gr.themes.Default(
-#     primary_hue="zinc",
-#     secondary_hue="red",
-#     neutral_hue="neutral",
-#     font=[gr.themes.GoogleFont('Roboto'), "sans-serif"]
-# )
-
-with gr.Blocks() as demo:
-
-  with gr.Row():
-      gr.Markdown("# Reference paper"),
-  with gr.Row():
-      top_k_box = gr.Slider(
+hf_writer = gr.HuggingFaceDatasetSaver(hf_token=token_w, dataset_name="ravistech/feedback-demo", private=True)
+top_k_box = gr.Slider(
           label="Amount of reference paper",
           value=5,
           minimum=0,
           maximum=30,
           step=1,
           )
-  # Study description
-  with gr.Row():
-      gr.Markdown("# Research Information"),
-  with gr.Row():
-      study_obj_box = gr.Textbox(
+study_obj_box = gr.Textbox(
           label="Study Objective / Study Description", # Study description
           placeholder=objective_place_holder,
           lines=10)
-  # Study Design
-  with gr.Row():
-      gr.Markdown("# Study Design"),
-  with gr.Column():
-      study_type_box = gr.Radio(
+
+study_type_box = gr.Radio(
           ["Expanded Access", "Interventional", "Observational"],
           label="Study Type",
           )
-      phase_box= gr.Radio(
+phase_box= gr.Radio(
           ["Not Applicable", "Early Phase 1", "Phase 1", "Phase 2", "Phase 3", "Phase 4"],
           label="Phase"
           )
-      purpose_box = gr.Radio(
+purpose_box = gr.Radio(
           ["Treatment", "Prevention", "Diagnostic", "Educational/Counseling/Training", "Supportive Care", "Screening", "Health Services Research", "Basic Science", "Device Feasibility", "Other"],
           label="Primary Purpose"
           )
-      allocation_box = gr.Radio(
+allocation_box = gr.Radio(
           ["Randomized", "Non-Randomized", "N/A"],
           label="Allocation"
           )
-      intervention_model_box = gr.Radio(
+intervention_model_box = gr.Radio(
           ["Parallel", "Single-Group", "Crossover", "Factorial", "Sequential"],
           label="Interventional Model"
           )
-      masking_box = gr.Radio(
+masking_box = gr.Radio(
           ["None (Open Label)", "Single", "Double", "Triple", "Quadruple"],
           label="Masking"
           )
-  # Conditions
-  with gr.Row():
-      gr.Markdown("# Conditions"),
-  with gr.Row():
-      conditions_box = gr.Textbox(
+conditions_box = gr.Textbox(
           label="Conditions / Disease",
-          info="Primary Disease or Condition of Cancer Being Studied in the Trial, or the Focus of the Study",
-          placeholder=conditions_place_holder,
           )
-  #Interventions
-  with gr.Row():
-      gr.Markdown("# Interventions / Drugs"),
-  with gr.Row():
-      intervention_box = gr.Textbox(
+intervention_box = gr.Textbox(
           label="Intervention type",
           placeholder=interventions_place_holder,
-          lines=10,
           )
-  #Location
-  with gr.Row():
-      gr.Markdown("# Location"),
-  with gr.Column():
-      location_box = gr.Textbox(
+location_box = gr.Textbox(
           label="Location (Countries)",
           )
-      removed_location_box = gr.Textbox(
+removed_location_box = gr.Textbox(
           label="Removed Location (Countries)",
           )
-
-  # Submit & Clear
-  with gr.Row():
-    submit_button = gr.Button("Submit")
-    clear_button = gr.Button("Clear")
-
-  # Output
-  with gr.Row():
-      gr.Markdown("# Eligibility Criteria Generation"),
-  with gr.Row():
-      with gr.Column():
-          base_box = gr.Textbox(
-              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)
-          
-  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]
-
-  submit_button.click(
-      run_function_on_text,
-      inputs=inputs_information,
-      outputs=outputs_information
-      )
-
-  clear_button.click(lambda : [None] * len(inputs_information), outputs=inputs_information)
-
-  with gr.Row():
-      selected_response = gr.Radio(
-          choices=[
+base_box = gr.Textbox(
+          label="Response 1",
+          lines=10,
+          interactive=False)
+rag_box = gr.Textbox(
+          label="Response 2",
+          lines=10,
+          interactive=False)
+
+
+demo = gr.Interface(
+  fn= run_function_on_text,
+  inputs=[
+      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=[
+      base_box,
+      rag_box],
+  title='Eligibility Criteria Generation',
+  # description='',
+  allow_flagging="manual",
+  flagging_options=[
               "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
-  token_w = os.environ['token_w']
-  hf_writer = gr.HuggingFaceDatasetSaver(hf_token=token_w, dataset_name="ravistech/feedback-demo", 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],"ravistech/feedback-demo")
-
-  flag_button.click(lambda *args: callback.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)
+  flagging_callback=hf_writer,
+)
 
-  with gr.Row():
-    clear_all_button = gr.Button("Clear All")
+demo.launch(debug=True)
+# custom_css = """
+# .gradio-container {
+#     font-family: 'Roboto', sans-serif;
+# }
+# .main-header {
+#     text-align: center;
+#     color: #4a4a4a;
+#     margin-bottom: 2rem;
+# }
+# .tab-header {
+#     font-size: 1.2rem;
+#     font-weight: bold;
+#     margin-bottom: 1rem;
+# }
+# .custom-chatbot {
+#     border-radius: 10px;
+#     box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+# }
+# .custom-button {
+#     background-color: #3498db;
+#     color: white;
+#     border: none;
+#     padding: 10px 20px;
+#     border-radius: 5px;
+#     cursor: pointer;
+#     transition: background-color 0.3s ease;
+# }
+# .custom-button:hover {
+#     background-color: #2980b9;
+# }
+# """
 
-  flag_response = [selected_response]
-  all_information = inputs_information + outputs_information + flag_response
-  clear_all_button.click(lambda : [None] * len(all_information), outputs=all_information)
+# # Define Gradio theme
+# theme = gr.themes.Default(
+#     primary_hue="zinc",
+#     secondary_hue="red",
+#     neutral_hue="neutral",
+#     font=[gr.themes.GoogleFont('Roboto'), "sans-serif"]
+# )
 
-if __name__ == "__main__":
-  demo.launch(debug=True)
+# with gr.Blocks() as demo:
+
+#   with gr.Row():
+#       gr.Markdown("# Reference paper"),
+#   with gr.Row():
+#       top_k_box = gr.Slider(
+#           label="Amount of reference paper",
+#           value=5,
+#           minimum=0,
+#           maximum=30,
+#           step=1,
+#           )
+#   # Study description
+#   with gr.Row():
+#       gr.Markdown("# Research Information"),
+#   with gr.Row():
+#       study_obj_box = gr.Textbox(
+#           label="Study Objective / Study Description", # Study description
+#           placeholder=objective_place_holder,
+#           lines=10)
+#   # Study Design
+#   with gr.Row():
+#       gr.Markdown("# Study Design"),
+#   with gr.Column():
+#       study_type_box = gr.Radio(
+#           ["Expanded Access", "Interventional", "Observational"],
+#           label="Study Type",
+#           )
+#       phase_box= gr.Radio(
+#           ["Not Applicable", "Early Phase 1", "Phase 1", "Phase 2", "Phase 3", "Phase 4"],
+#           label="Phase"
+#           )
+#       purpose_box = gr.Radio(
+#           ["Treatment", "Prevention", "Diagnostic", "Educational/Counseling/Training", "Supportive Care", "Screening", "Health Services Research", "Basic Science", "Device Feasibility", "Other"],
+#           label="Primary Purpose"
+#           )
+#       allocation_box = gr.Radio(
+#           ["Randomized", "Non-Randomized", "N/A"],
+#           label="Allocation"
+#           )
+#       intervention_model_box = gr.Radio(
+#           ["Parallel", "Single-Group", "Crossover", "Factorial", "Sequential"],
+#           label="Interventional Model"
+#           )
+#       masking_box = gr.Radio(
+#           ["None (Open Label)", "Single", "Double", "Triple", "Quadruple"],
+#           label="Masking"
+#           )
+#   # Conditions
+#   with gr.Row():
+#       gr.Markdown("# Conditions"),
+#   with gr.Row():
+#       conditions_box = gr.Textbox(
+#           label="Conditions / Disease",
+#           info="Primary Disease or Condition of Cancer Being Studied in the Trial, or the Focus of the Study",
+#           placeholder=conditions_place_holder,
+#           )
+#   #Interventions
+#   with gr.Row():
+#       gr.Markdown("# Interventions / Drugs"),
+#   with gr.Row():
+#       intervention_box = gr.Textbox(
+#           label="Intervention type",
+#           placeholder=interventions_place_holder,
+#           lines=10,
+#           )
+#   #Location
+#   with gr.Row():
+#       gr.Markdown("# Location"),
+#   with gr.Column():
+#       location_box = gr.Textbox(
+#           label="Location (Countries)",
+#           )
+#       removed_location_box = gr.Textbox(
+#           label="Removed Location (Countries)",
+#           )
+
+#   # Submit & Clear
+#   with gr.Row():
+#     submit_button = gr.Button("Submit")
+#     clear_button = gr.Button("Clear")
+
+#   # Output
+#   with gr.Row():
+#       gr.Markdown("# Eligibility Criteria Generation"),
+#   with gr.Row():
+#       with gr.Column():
+#           base_box = gr.Textbox(
+#               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)
+          
+#   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]
+
+#   submit_button.click(
+#       run_function_on_text,
+#       inputs=inputs_information,
+#       outputs=outputs_information
+#       )
+
+#   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
+#   token_w = os.environ['token_w']
+#   hf_writer = gr.HuggingFaceDatasetSaver(hf_token=token_w, dataset_name="ravistech/feedback-demo", 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],"ravistech/feedback-demo")
+
+#   flag_button.click(lambda *args: callback.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():
+#     clear_all_button = gr.Button("Clear All")
+
+#   flag_response = [selected_response]
+#   all_information = inputs_information + outputs_information + flag_response
+#   clear_all_button.click(lambda : [None] * len(all_information), outputs=all_information)
+
+# if __name__ == "__main__":
+#   demo.launch(debug=True)