Add application file

app.py

@@ -0,0 +1,175 @@
+import gradio as gr
+
+import pandas as pd
+from datasets import Dataset
+from sentence_transformers import SentenceTransformer
+from parameter_extractor import ParameterExtractor
+from DNAseq import DNAseq
+from helper import list_at_index_0, list_at_index_1, logger
+
+
+def chat_to_sequence(sequence, user_query):
+    if sequence is None:
+        gr.Warning("Sequence Is Empty. Please Input A Sequence")
+    if user_query is None:
+        gr.Warning("Query Is Empty. Please Input A Query")
+    # Log information to a CSV file
+    log_filename = "CTS_user_log.csv"
+
+    # Sequence to be analysed/queried
+    input_sequence = sequence
+
+    # Set ParameterExtractor class expected variable
+    dna = input_sequence
+
+    # Model
+    model_name = "all-mpnet-base-v2"
+
+    # Load model
+    model = SentenceTransformer(model_name)
+
+    # User input
+    user_query = user_query
+
+    # Set ParameterExtractor class expected variable
+    query = user_query
+
+    # Bot Response
+    response = ""
+
+    # Query Code Description Message
+    code_descript_message = ''
+
+    # kNN semantic similarity threshold / used to determine if query can execute code
+    # kNN semantic similarity values less than the lower threshold should return a code eval response
+    # kNN semantic similarity values more than the lower threshold shouldn't return a code eval response
+    proximal_lower_threshold = 1.1
+    proximal_upper_threshold = 1.4
+
+    threshold_exceeded_message = "Your Query Wasn't Understood. Can You Rephrase The Query"
+    threshold_approximate_message = "Your Query Wasn't Understood Clearly. Try Using The Following Query Formats"
+
+    # Load the function mapping CSV file into a pandas DataFrame
+    code_function_mapping = pd.read_csv("code_function_mapping.csv")
+
+    # Load reference query database from JSON file back into a DataFrame
+    ref_query_df = pd.read_json('reference_query_db.json', orient='records')
+
+    # Create Dataset object using the pandas data frame
+    ref_query_ds = Dataset.from_pandas(ref_query_df)
+
+    # Load FAISS index
+    ref_query_ds.load_faiss_index('all-mpnet-base-v2_embeddings', 'ref_query_db_index')
+
+    # Create embeddings for user query
+    query_embedding = model.encode(user_query)
+
+    # Semantic similarity search user query against sample queries
+    index_result = ref_query_ds.get_nearest_examples("all-mpnet-base-v2_embeddings", query_embedding, k=3)
+    print(index_result)
+
+    # Retrieve results from dataset object
+    scores, examples = index_result
+
+    # Create a DataFrame from the examples dictionary
+    result_df = pd.DataFrame(examples)
+
+    # Add the scores as a new column to the DataFrame
+    result_df['score'] = scores
+
+    # Sort the DataFrame by the 'Score' column in ascending order
+    # FIASS uses kNN as the similarity algorithm / value of 0 indicates an exact match
+    sorted_df = result_df.sort_values(by='score', ascending=True)
+
+    # Get the query with the lowest kNN score (first row after sorting)
+    ref_question = sorted_df.iloc[0]['question']
+
+    # Get the code for the query with the lowest kNN score (first row after sorting)
+    query_code = sorted_df.iloc[0]['code']
+
+    # Get the score for the query with the lowest kNN score (first row after sorting)
+    query_score = sorted_df.iloc[0]['score']
+
+    # Description of query code to be executed
+    query_code_description = code_function_mapping[code_function_mapping['code'] == query_code]['description'].values[0]
+
+    # Print the query with the highest score
+    print(ref_question, query_code, query_score)
+    similarity_metric = "k nearest neighbours"
+
+    ref_question_2 = sorted_df.iloc[1]['question']
+    ref_question_3 = sorted_df.iloc[1]['question']
+    query_score_2 = sorted_df.iloc[1]['score']
+    query_score_3 = sorted_df.iloc[1]['score']
+
+    log_data = [
+        user_query,
+        ref_question,
+        query_score,
+        query_code,
+        ref_question_2,
+        query_score_2,
+        ref_question_3,
+        query_score_3,
+        similarity_metric,
+        model_name,
+        proximal_lower_threshold,
+        proximal_upper_threshold,
+    ]
+    # Check the query score against threshold values
+    if query_score >= proximal_upper_threshold:
+        response = threshold_exceeded_message
+        logger(log_filename, log_data, response)
+        print(threshold_exceeded_message)
+
+    elif proximal_lower_threshold < query_score < proximal_upper_threshold:
+        response = threshold_approximate_message + "/n" + ref_question
+        logger(log_filename, log_data, response)
+        print(threshold_approximate_message, ref_question)
+    else:
+        print("Execute query")
+        # Define the question
+        code = query_code
+
+        # Filter the DataFrame to find the code that matches the question
+        matching_row = code_function_mapping[code_function_mapping["code"] == code]
+
+        # Check if there is a match
+        if not matching_row.empty:
+            function = matching_row.iloc[0]["function"]
+            response = str(eval(function))
+            code_descript_message = query_code_description.title()
+            logger(log_filename, log_data, response)
+        else:
+            response = "Error processing query"
+            query_code = "No Match Error"
+            logger(log_filename, log_data, response)
+            print("No matching code found for the function:", code)
+
+        return response, code_descript_message
+    return response, code_descript_message
+
+
+ChatToSequence = gr.Interface(
+    fn=chat_to_sequence,
+    inputs=[gr.Textbox(label="Sequence", placeholder="Input DNA Sequence..."),
+            gr.Textbox(label="Query", placeholder="Input Query...")],
+    outputs=[gr.Textbox(label="Response"), gr.Textbox(label="Action Executed")],
+    title="Chat-To-Sequence",
+    description="This Demo App Allows You To Explore Your DNA Sequence Using Natural Language",
+    theme=gr.themes.Soft(),
+    examples=[
+        ["ggcattgaggagaccattgacaccgtcattagcaatgcactacaactgtcacaacctaaa",
+         "What is the length of the sequence"],
+        ["ggcattgaggagaccattgacaccgtcattagcaatgcactacaactgtcacaacctaaa",
+         "How many guanines bases are there in the sequence"],
+        ["ggcattgaggagaccattgacaccgtcattagcaatgcactacaactgtcacaacctaaa",
+         "What is the base at position 10"],
+        ["ggcattgaggagaccattgacaccgtcattagcaatgcactacaactgtcacaacctaaa",
+         "What are the bases from position 2 to 10"],
+        ["ggcattgaggagaccattgacaccgtcattagcaatgcactacaactgtcacaacctaaa",
+         "How many bases are there from position 2 to 10"],
+    ],
+).queue()
+
+ChatToSequence.launch(share=True)