Update app

.gitignore

@@ -0,0 +1 @@
+.venv

app.old.py

@@ -0,0 +1,183 @@
+import os
+import pandas as pd
+# import matplotlib.pyplot as plt
+import gradio as gr
+import numpy as np
+import xgboost_infer
+
+# def predict_genus_dna(dnaSeqs):
+#     genuses = []
+
+#     # probs = dnamodel.predict_proba(dnaSeqs)
+#     # preds = dnamodel.predict(dnaSeqs)
+#     # topProb = np.argsort(probs, axis=1)[:,-3:]
+#     # topClass = dnamodel.classes_[topProb]
+
+#     # pred_df = pd.DataFrame(data=[topClass, topProb], columns= ['Genus', 'Probability'])
+
+#     return genuses
+
+# def predict_genus_dna_env(dnaSeqsEnv):
+#     genuses = {}
+#     probs = model.predict_proba(dnaSeqsEnv)
+#     preds = model.predict(dnaSeqsEnv)
+    
+#     for i in range(len(dnaSeqsEnv)):
+#         topProb = np.argsort(probs[i], axis=1)[:,-3:]
+#         topClass = model.classes_[topProb]
+
+#         sampleStr = dnaSeqsEnv['nucraw'][i]
+#         genuses[sampleStr] = (topClass, topProb)
+
+    # pred_df = pd.DataFrame(data=[top5class, top5prob], columns= ['Genus', 'Probability'])
+
+    # return genuses
+
+# def get_genus_image(genus):
+#     # return a URL to genus image
+#     return f"https://example.com/images/{genus}.jpg"
+
+def get_genuses(dna_file, dnaenv_file):
+    dna_df = pd.read_csv(dna_file.name)
+    dnaenv_df = pd.read_csv(dnaenv_file.name)
+    
+    results = []
+    
+    # envdna_genuses = predict_genus_dna_env(dnaenv_df)
+    # dna_genuses = predict_genus_dna(dna_df)
+    # images = [get_genus_image(genus) for genus in top_5_genuses]
+
+    genuses = xgboost_infer.infer()
+        
+    results.append({
+        "sequence": dna_df['nucraw'],
+        # "predictions": pd.concat([dna_genuses, envdna_genuses], axis=0)
+        'predictions': genuses
+})
+    
+    return results
+
+def display_results(results):
+    display = []
+    for result in results:
+        # for i in range(len(result["predictions"])):
+        #     display.append({
+        #         "DNA Sequence": result["sequence"],
+        #         "DNA Pred Genus": result['predictions'][i][0],
+        #         "DNA Only Prob": result['predictions'][i][1],
+        #         "DNA Env Pred Genus": result['predictions'][i][2],
+        #         "DNA Env Prob": result['predictions'][i][3],
+        #         # "Image": result["images"][i]
+        #     })
+        display.append({
+            "DNA Sequence": result["sequence"],
+            "DNA Pred Genus": result['predictions'][0]
+        })
+    return pd.DataFrame(display)
+
+def gradio_interface(file):
+    results = get_genuses(file)
+    return display_results(results)
+
+# Gradio interface
+with gr.Blocks() as demo:
+    with gr.Column():
+        gr.Markdown("# DNA Identifier Tool")
+        file_input = gr.File(label="Upload DNA CSV file", file_types=['csv'])
+        output_table = gr.Dataframe(headers=["DNA", "Coord", "DNA Only Pred Genus", "DNA Only Prob", "DNA & Env Pred Genus", "DNA & Env Prob"])
+        
+        def update_output(file):
+            result_df = gradio_interface(file)
+            return result_df
+        
+        file_input.change(update_output, inputs=file_input, outputs=output_table)
+        
+demo.launch()
+
+
+# with gr.Blocks() as demo:
+#     with gr.Row():
+#         word = gr.Textbox(label="word")
+#         leng = gr.Number(label="leng")
+#         output = gr.Textbox(label="Output")
+#     with gr.Row():
+#         run = gr.Button()
+
+#     event = run.click(predict_genus, 
+#                       [word, leng], 
+#                       output, 
+#                       batch=True, 
+#                       max_batch_size=20)
+
+# demo.launch()
+
+# DB_USER = os.getenv("DB_USER")
+# DB_PASSWORD = os.getenv("DB_PASSWORD")
+# DB_HOST = os.getenv("DB_HOST")
+# PORT = 8080
+# DB_NAME = "bikeshare"
+
+# connection_string = f"postgresql://{DB_USER}:{DB_PASSWORD}@{DB_HOST}?port={PORT}&dbname={DB_NAME}"
+
+# def get_count_ride_type():
+#     df = pd.read_sql(
+#     """
+#         SELECT COUNT(ride_id) as n, rideable_type
+#         FROM rides
+#         GROUP BY rideable_type
+#         ORDER BY n DESC
+#     """,
+#     con=connection_string
+#     )
+#     fig_m, ax = plt.subplots()
+#     ax.bar(x=df['rideable_type'], height=df['n'])
+#     ax.set_title("Number of rides by bycycle type")
+#     ax.set_ylabel("Number of Rides")
+#     ax.set_xlabel("Bicycle Type")
+#     return fig_m
+
+
+# def get_most_popular_stations():
+
+#     df = pd.read_sql(
+#         """
+#     SELECT COUNT(ride_id) as n, MAX(start_station_name) as station
+#     FROM RIDES
+#     WHERE start_station_name is NOT NULL
+#     GROUP BY start_station_id
+#     ORDER BY n DESC
+#     LIMIT 5
+#     """,
+#     con=connection_string
+#     )
+#     fig_m, ax = plt.subplots()
+#     ax.bar(x=df['station'], height=df['n'])
+#     ax.set_title("Most popular stations")
+#     ax.set_ylabel("Number of Rides")
+#     ax.set_xlabel("Station Name")
+#     ax.set_xticklabels(
+#         df['station'], rotation=45, ha="right", rotation_mode="anchor"
+#     )
+#     ax.tick_params(axis="x", labelsize=8)
+#     fig_m.tight_layout()
+#     return fig_m
+
+
+# with gr.Blocks() as demo:
+#     with gr.Row():
+#         bike_type = gr.Plot()
+#         station = gr.Plot()
+
+#     demo.load(get_count_ride_type, inputs=None, outputs=bike_type)
+#     demo.load(get_most_popular_stations, inputs=None, outputs=station)
+
+# def greet(name, intensity):
+#     return "Hello, " + name + "!" * int(intensity)
+
+# demo = gr.Interface(
+#     fn=greet,
+#     inputs=["text", "slider"],
+#     outputs=["text"],
+# )
+
+demo.launch()

app.py

@@ -1,183 +1,34 @@
-import os
-import pandas as pd
-# import matplotlib.pyplot as plt
-import gradio as gr
-import numpy as np
-import xgboost_infer
-
-# def predict_genus_dna(dnaSeqs):
-#     genuses = []
-
-#     # probs = dnamodel.predict_proba(dnaSeqs)
-#     # preds = dnamodel.predict(dnaSeqs)
-#     # topProb = np.argsort(probs, axis=1)[:,-3:]
-#     # topClass = dnamodel.classes_[topProb]
-
-#     # pred_df = pd.DataFrame(data=[topClass, topProb], columns= ['Genus', 'Probability'])
-
-#     return genuses
-
-# def predict_genus_dna_env(dnaSeqsEnv):
-#     genuses = {}
-#     probs = model.predict_proba(dnaSeqsEnv)
-#     preds = model.predict(dnaSeqsEnv)
-    
-#     for i in range(len(dnaSeqsEnv)):
-#         topProb = np.argsort(probs[i], axis=1)[:,-3:]
-#         topClass = model.classes_[topProb]
+import json
 
-#         sampleStr = dnaSeqsEnv['nucraw'][i]
-#         genuses[sampleStr] = (topClass, topProb)
-
-    # pred_df = pd.DataFrame(data=[top5class, top5prob], columns= ['Genus', 'Probability'])
-
-    # return genuses
+import gradio as gr
 
-# def get_genus_image(genus):
-#     # return a URL to genus image
-#     return f"https://example.com/images/{genus}.jpg"
 
-def get_genuses(dna_file, dnaenv_file):
-    dna_df = pd.read_csv(dna_file.name)
-    dnaenv_df = pd.read_csv(dnaenv_file.name)
-    
-    results = []
-    
-    # envdna_genuses = predict_genus_dna_env(dnaenv_df)
-    # dna_genuses = predict_genus_dna(dna_df)
-    # images = [get_genus_image(genus) for genus in top_5_genuses]
+with open("default_inputs.json", "r") as default_inputs_file:
+    DEFAULT_INPUTS = json.load(default_inputs_file)
 
-    genuses = xgboost_infer.infer()
-        
-    results.append({
-        "sequence": dna_df['nucraw'],
-        # "predictions": pd.concat([dna_genuses, envdna_genuses], axis=0)
-        'predictions': genuses
-})
-    
-    return results
 
-def display_results(results):
-    display = []
-    for result in results:
-        # for i in range(len(result["predictions"])):
-        #     display.append({
-        #         "DNA Sequence": result["sequence"],
-        #         "DNA Pred Genus": result['predictions'][i][0],
-        #         "DNA Only Prob": result['predictions'][i][1],
-        #         "DNA Env Pred Genus": result['predictions'][i][2],
-        #         "DNA Env Prob": result['predictions'][i][3],
-        #         # "Image": result["images"][i]
-        #     })
-        display.append({
-            "DNA Sequence": result["sequence"],
-            "DNA Pred Genus": result['predictions'][0]
-        })
-    return pd.DataFrame(display)
+def set_default_inputs():
+    return (DEFAULT_INPUTS["dna_sequence"],
+            DEFAULT_INPUTS["latitude"],
+            DEFAULT_INPUTS["longitude"])
 
-def gradio_interface(file):
-    results = get_genuses(file)
-    return display_results(results)
 
-# Gradio interface
 with gr.Blocks() as demo:
-    with gr.Column():
-        gr.Markdown("# DNA Identifier Tool")
-        file_input = gr.File(label="Upload DNA CSV file", file_types=['csv'])
-        output_table = gr.Dataframe(headers=["DNA", "Coord", "DNA Only Pred Genus", "DNA Only Prob", "DNA & Env Pred Genus", "DNA & Env Prob"])
-        
-        def update_output(file):
-            result_df = gradio_interface(file)
-            return result_df
-        
-        file_input.change(update_output, inputs=file_input, outputs=output_table)
-        
-demo.launch()
-
-
-# with gr.Blocks() as demo:
-#     with gr.Row():
-#         word = gr.Textbox(label="word")
-#         leng = gr.Number(label="leng")
-#         output = gr.Textbox(label="Output")
-#     with gr.Row():
-#         run = gr.Button()
+    # Header section
+    gr.Markdown("# DNA Identifier Tool")
+    gr.Markdown("TODO short description of the tool...")
 
-#     event = run.click(predict_genus, 
-#                       [word, leng], 
-#                       output, 
-#                       batch=True, 
-#                       max_batch_size=20)
+    # Collect inputs for app (DNA and location)
+    with gr.Row():
+        inp_dna = gr.Textbox(label="DNA", placeholder="e.g. AACAATGTA... (will be automatically truncated to 660 characters)")
+    with gr.Row():
+        inp_lat = gr.Textbox(label="Latitude", placeholder="e.g. -3.009083")
+        inp_lng = gr.Textbox(label="Longitude", placeholder="e.g. -58.68281")
 
-# demo.launch()
+    with gr.Row():
+        btn_run = gr.Button("Run")
 
-# DB_USER = os.getenv("DB_USER")
-# DB_PASSWORD = os.getenv("DB_PASSWORD")
-# DB_HOST = os.getenv("DB_HOST")
-# PORT = 8080
-# DB_NAME = "bikeshare"
+        btn_defaults = gr.Button("I'm feeling lucky")
+        btn_defaults.click(fn=set_default_inputs, outputs=[inp_dna, inp_lat, inp_lng])
 
-# connection_string = f"postgresql://{DB_USER}:{DB_PASSWORD}@{DB_HOST}?port={PORT}&dbname={DB_NAME}"
-
-# def get_count_ride_type():
-#     df = pd.read_sql(
-#     """
-#         SELECT COUNT(ride_id) as n, rideable_type
-#         FROM rides
-#         GROUP BY rideable_type
-#         ORDER BY n DESC
-#     """,
-#     con=connection_string
-#     )
-#     fig_m, ax = plt.subplots()
-#     ax.bar(x=df['rideable_type'], height=df['n'])
-#     ax.set_title("Number of rides by bycycle type")
-#     ax.set_ylabel("Number of Rides")
-#     ax.set_xlabel("Bicycle Type")
-#     return fig_m
-
-
-# def get_most_popular_stations():
-
-#     df = pd.read_sql(
-#         """
-#     SELECT COUNT(ride_id) as n, MAX(start_station_name) as station
-#     FROM RIDES
-#     WHERE start_station_name is NOT NULL
-#     GROUP BY start_station_id
-#     ORDER BY n DESC
-#     LIMIT 5
-#     """,
-#     con=connection_string
-#     )
-#     fig_m, ax = plt.subplots()
-#     ax.bar(x=df['station'], height=df['n'])
-#     ax.set_title("Most popular stations")
-#     ax.set_ylabel("Number of Rides")
-#     ax.set_xlabel("Station Name")
-#     ax.set_xticklabels(
-#         df['station'], rotation=45, ha="right", rotation_mode="anchor"
-#     )
-#     ax.tick_params(axis="x", labelsize=8)
-#     fig_m.tight_layout()
-#     return fig_m
-
-
-# with gr.Blocks() as demo:
-#     with gr.Row():
-#         bike_type = gr.Plot()
-#         station = gr.Plot()
-
-#     demo.load(get_count_ride_type, inputs=None, outputs=bike_type)
-#     demo.load(get_most_popular_stations, inputs=None, outputs=station)
-
-# def greet(name, intensity):
-#     return "Hello, " + name + "!" * int(intensity)
-
-# demo = gr.Interface(
-#     fn=greet,
-#     inputs=["text", "slider"],
-#     outputs=["text"],
-# )
-
-demo.launch()
+demo.launch()

default_inputs.json

@@ -0,0 +1,5 @@
+{
+    "dna_sequence": "AACAATGTATTTGATTTTCGCCCTTGTGAATTTATTCGCTGGCGGAACAATGGCATTGTTGATTCGTTTGGAGTTGTTCCAACCTGGCTTGCAATTTTTAAGACCTGAGTTTTTTAATCAGTTAACAACTATGCACGGCCTTATAATGGTTTTCGGTGCAATTATGCCGGCCTTTGTGGGTTTTGCTAACTTGATGATTCCTTTGCAAATTGGTGCCTCTGATATGGCGTTTGCAAGAATGAACAATTTTAGTTTCTGGATTATGCCTGTTGCAGGGATGTTATTATTTGGCTCATTTTTGGCTCCTGGTGGCGCTACTGCAGCTGGTTGGACTTTGTATGCTCCTTTGTCGGTCCAAATGGGGCCTGGTATGGACATGACTATTTTTGCTGTTCACTTGATGGGTGCTTCATCCATTATGGGATCCATTAATATCATTGTGACAATTCTGAATATGCGTGCTCCTGGACTGTCTTTGATGAAGATGCCAATGTTCTGTTGGACATGGTTGATTACTGCATATTTGTTAATTGCGGTTATGCCTGTTTTAGCTGGTGCTATCACTATGGTTCTAACAGACCGTCACTTTGGAACAAGCTTTTTTGCAGCTGCTGGCGGTGGAGACCCTGTAATGTATCAACATATCTTC",
+    "latitude": "-3.009083",
+    "longitude": "-58.68281"
+}

requirements.txt

@@ -3,6 +3,7 @@ pandas==2.2.2
 torch==2.3.0
 tqdm==4.66.4
 transformers==4.41.2
-sklearn
-numpy
-datasets
+scikit-learn==1.5.0
+numpy==1.26.4
+datasets==2.19.1
+gradio==4.32.2