pseudocode for pap

app.py

@@ -1,12 +1,176 @@
+import os
+import pandas as pd
+import matplotlib.pyplot as plt
 import gradio as gr
+import numpy as mp
 
-def greet(name, intensity):
-    return "Hello, " + name + "!" * int(intensity)
 
-demo = gr.Interface(
-    fn=greet,
-    inputs=["text", "slider"],
-    outputs=["text"],
-)
+def predict_genus_dna(dnaSeqs):
+    genuses = []
+
+    probs = dnamodel.predict_proba(dnaSeqs)
+    preds = dnamodel.predict(dnaSeqs)
+    top5prob = np.argsort(probs, axis=1)[:,-n:]
+    top5class = dnamodel.classes_[top5prob]
+
+    pred_df = pd.DataFrame(data=[top5class, top5prob], 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)):
+        top5prob = np.argsort(probs[i], axis=1)[:,-5:]
+        top5class = model.classes_[top5prob]
+
+        sampleStr = dnaSeqsEnv['nucraw'][i]
+        genuses[sampleStr] = (top5class, top5prob)
+
+    # 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]
+        
+    results.append({
+        "sequence": dna_sequence,
+        "Predictions": envdna_genuses + dna_genuses,
+        # "images": images
+    })
+    
+    return results
+
+def display_results(results):
+    display = []
+    for result in results:
+        for i in range(len(result["predictions"])):
+            display.append({
+                "DNA Sequence": result["sequence"],
+                "Predicted Genus": result['predictions'][i][0],
+                "Predicted Genus": result['predictions'][i][0],
+                "Predicted Genus": result['predictions'][i][0],
+                # "Image": result["images"][i]
+            })
+    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("# Top 5 Most Likely Genus Predictions")
+        file_input = gr.File(label="Upload 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()