app.py

import os
import time
import asyncio
from llama_index.core.query_engine import CitationQueryEngine
from llama_index.core import VectorStoreIndex
from llama_index.core import Settings
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.llms.gemini import Gemini
from llama_index.core.postprocessor import SimilarityPostprocessor
from llama_index.core.storage.docstore import SimpleDocumentStore
from llama_index.core import StorageContext, load_index_from_storage

HF_TOKEN=os.environ['token_r']
API_KEY=os.environ["GOOGLE_API_KEY"]

generation_config = {
  "temperature": 0,
  # "top_p": 1,
  # "top_k": 1,
  "max_output_tokens":8192,
}

safety_settings = [
  {
    "category": "HARM_CATEGORY_HARASSMENT",
    "threshold": "BLOCK_NONE"
  },
  {
    "category": "HARM_CATEGORY_HATE_SPEECH",
    "threshold": "BLOCK_NONE"
  },
  {
    "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
    "threshold": "BLOCK_NONE"
  },
  {
    "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
    "threshold": "BLOCK_NONE"
  },
]

llm = Gemini(
    model="models/gemini-1.5-flash-002",
    generation_config=generation_config,
    safety_settings=safety_settings,
)

# Setup embedder
embed_model_name = "BAAI/bge-small-en-v1.5"
embed_model = HuggingFaceEmbedding(model_name=embed_model_name)

Settings.llm = llm
Settings.embed_model = embed_model

# rebuild storage context
storage_context = StorageContext.from_defaults(persist_dir="VectorStore")
# load index
index_persisted = load_index_from_storage(storage_context, index_id="vector_index")


async def run_function_on_text(top_k,study_obj,study_type,phase,purpose,allocation,intervention_model,Masking,conditions,interventions,location_countries,removed_location_countries):

  # Set up query engine
  query_engine_get_study = CitationQueryEngine.from_args(
    index_persisted,
    similarity_top_k=top_k,
    citation_chunk_size=2048,
    verbose=True,
    node_postprocessors=[SimilarityPostprocessor(similarity_cutoff=0.8)],
    use_async=True
  )

  #Build prompt
  study_information = f"""
  #Study Objectives/Study Description
  {study_obj}

  #Intervention
  {interventions}

  #Location
    - Location_Countries: {location_countries}
    - Removed Location: {removed_location_countries}

  #Conditions
   Cancer {conditions}

  #Study Design
    - Study Type: {study_type}
    - Phase: {phase}
    - Primary Purpose: {purpose}
    - Allocation: {allocation}
    - Interventional Model: {intervention_model}
    - Masking: None {Masking}
  """

  # Query
  query_response = await query_engine_get_study.aquery(f"""
      Based on the provided instructions and clinical trial information, generate the new eligibility criteria by analyzing the related studies and clinical trial information.
      ### Instruction:
      Find suitable papers that have relevant or similar to the clinical trial information(### Clinical Trial Information).
      Prioritize the following topics when finding related studies:
      1. Study Objectives
      2. Study Design and Phases
      3. Conditions
      4. Intervention/Treatment

      Criteria generation:
      As a clinical researcher, generate new eligibility criteria for given clinical trial information.
      Analyze the information from related studies for more precise new eligibility criteria generation.
      Ensure the criteria are clear, specific, and reasonable for a clinical research information.
      Do not generate [<number of citation>].

      Reference Papers generation:
      Please give us NCT IDs and study names for {top_k} used papers.

      Please follows 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.
      .
      .
      .

      Reference Papers
      1.NCT ID:
        Study Name:
        Condition:
        Intervention/Treatment:
      2.NCT ID:
        Study Name:
        Condition:
        Intervention/Treatment:
      .
      .
      .


      """
    )

  # 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:
      .
      .
      .
      """
    )

  return query_response,complete_response,combine_response

# Place holder
place_holder = f"""Study Objectives
The purpose of this study is to evaluate the safety, tolerance and efficacy of Liposomal Paclitaxel With Nedaplatin as First-line in patients with Advanced or Recurrent Esophageal Carcinoma

Conditions: Esophageal Carcinoma

Intervention / Treatment:
DRUG: Liposomal Paclitaxel,
DRUG: Nedaplatin

Location: China

Study Design and Phases
Study Type: INTERVENTIONAL
Phase: PHASE2 Primary Purpose:
TREATMENT Allocation: NA
Interventional Model: SINGLE_GROUP Masking: NONE
"""

objective_place_holder = f"""Example: The purpose of this study is to evaluate the safety, tolerance and efficacy of Liposomal Paclitaxel With Nedaplatin as First-line in patients with Advanced or Recurrent Esophageal Carcinoma
"""

conditions_place_holder = f"""Example: Esophageal Carcinoma
"""

interventions_place_holder = f"""Example:
- Drug: irinotecan hydrochloride
   - Given IV
   - Other Names:
       - Campto
       - Camptosar
       - CPT-11
       - irinotecan
       - U-101440E

- Drug: Amoxicillin hydrate
   - Amoxicillin hydrate (potency)

- Procedure: Stem cell transplant
   - See Detailed Description section for details of treatment interventions.

- Biological: Pneumococcal Vaccine
   - Subcutaneously on Day 0
   - Other Names:
       - Prevnar

- Drug: Doxorubicin, Cotrimoxazole, Carboplatin, Ifosfamide

- Drug: Irinotecan
   - Irinotecan will be administered at a dose of 180mg/m2 IV over 90 minutes on day 21 every 42 days.
   - Other Names:
      - CAMPTOSAR™

- Drug: Placeblo
   - Placebo tablet
"""

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(
          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,
          )
  #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=5,
              interactive=False)
      with gr.Column():
          rag_box = gr.Textbox(
              label="Response 2",
              lines=5,
              interactive=False)
  with gr.Row():
      with gr.Column():
          combine_box = gr.Textbox(
              label="Response 3",
              lines=5,
              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 is better",
              "Response 2 is better",
              "Both responses are equally good",
              "Neither response is satisfactory"
              ],
          label="Select the better response"
      )
  with gr.Row():
      flag_button = gr.Button("Flag Selected Response")

  #Flagging
  callback = gr.CSVLogger()
  callback.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], "flagged_data_points")

  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], None, preprocess=False)

  with gr.Row():
    clear_all_button = gr.Button("Clear All")

  output_information = inputs_information + outputs_information
  clear_all_button.click(lambda : [None] * len(output_information), outputs=output_information)

if __name__ == "__main__":
  demo.launch(debug=True)