adding files

Dockerfile

@@ -1,15 +1,3 @@
-# FROM python:3.9
-
-# WORKDIR /code
-
-# COPY ./requirements.txt /code/requirements.txt
-
-# RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
-
-# COPY . .
-
-# CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]
-
 # Use the official Python image
 FROM python:3.9
 
@@ -29,4 +17,4 @@ RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
 COPY . /code/
 
 # Command to run on container start
-CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8123"]

gradio_app.py

@@ -1,21 +0,0 @@
-import gradio as gr
-import pandas as pd
-
-simple = pd.DataFrame(
-    {
-        "item": ["A", "B", "C", "D", "E", "F", "G", "H", "I"],
-        "inventory": [28, 55, 43, 91, 81, 53, 19, 87, 52],
-    }
-)
-
-with gr.Blocks() as demo:
-    gr.BarPlot(
-        value=simple,
-        x="item",
-        y="inventory",
-        title="Simple Bar Plot",
-        container=False,
-    )
-
-if __name__ == "__main__":
-    demo.launch(share=True)

main.py

@@ -1,28 +1,70 @@
-from fastapi import FastAPI, HTTPException
-from fastapi.responses import HTMLResponse
-import matplotlib.pyplot as plt
-from io import BytesIO
-import base64
-from gradio_client import Client
+from fastapi import FastAPI, File, UploadFile, Form
+import pandas as pd
+from google.cloud import storage
+import io
+import os
 
 app = FastAPI()
 
-@app.post("/predict")
-async def predict(
-    name1: str, score1: int,
-    name2: str, score2: int,
-    name3: str, score3: int,
-    name4: str, score4: int,
-    name5: str, score5: int
-):
-    # Create a Gradio client and make a prediction
-    gradio_client = Client("https://deepak6593-gradio-app.hf.space/--replicas/9890g/")
-    result = gradio_client.predict(
-        name1, score1, name2, score2, name3, score3, name4, score4, name5, score5,
-        api_name="/predict"
-    )
-
-    # HTML response containing the iframe
-    iframe_html = f'<iframe src="{result['plot']}" width="600" height="400"></iframe>'
-    
-    return HTMLResponse(content=iframe_html)
+# GCS credentials
+gcs_credentials_file = "ow-stu-us-ce1-dev.json"
+gcs_bucket_name = "ow-stu-us-ce1-ai-platform"
+
+# Ensure the GCS bucket exists
+gcs_client = storage.Client.from_service_account_json(gcs_credentials_file)
+gcs_bucket = gcs_client.bucket(gcs_bucket_name)
+
+# File path in GCS bucket
+gcs_file_path = "deepak_6593/db.csv"
+
+def append_to_gcs_csv(new_data, gcs_file_path):
+    # Standardize column names for new data
+    new_data.columns = ['category', 'score']
+
+    # Check if the file exists in GCS bucket
+    blob = gcs_bucket.blob(gcs_file_path)
+    exists = blob.exists()
+
+    # If file exists, read it into a DataFrame
+    if exists:
+        existing_data = pd.read_csv(io.BytesIO(blob.download_as_bytes()))
+        # Ensure existing data has the right columns
+        existing_data = existing_data[['category', 'score']]
+        # Append new data to existing data
+        combined_data = pd.concat([existing_data, new_data], ignore_index=True)
+    else:
+        combined_data = new_data
+
+    # Convert combined DataFrame to CSV and upload it
+    csv_data = combined_data.to_csv(index=False).encode('utf-8')
+    blob.upload_from_string(csv_data, content_type='text/csv')
+
+def read_from_gcs_csv(gcs_file_path):
+    blob = gcs_bucket.blob(gcs_file_path)
+    return pd.read_csv(io.BytesIO(blob.download_as_text()))
+
+@app.post("/upload-file/")
+async def upload_file(file: UploadFile = File(...)):
+    df = pd.read_csv(io.StringIO((await file.read()).decode('utf-8')))
+    append_to_gcs_csv(df, gcs_file_path)
+    return {"message": "File uploaded successfully"}
+
+@app.post("/upload-data/")
+async def upload_data(category: str = Form(...), score: float = Form(...)):
+    df = pd.DataFrame([[category, score]], columns=['category', 'score'])
+    append_to_gcs_csv(df, gcs_file_path)
+    return {"message": "Data uploaded successfully"}
+
+@app.post("/clear-data/")
+async def clear_data():
+    # Create an empty DataFrame with the same columns
+    empty_df = pd.DataFrame(columns=['category', 'score'])
+    # Convert the empty DataFrame to CSV
+    csv_data = empty_df.to_csv(index=False).encode('utf-8')
+    # Overwrite the existing file in GCS with the empty CSV data
+    gcs_bucket.blob(gcs_file_path).upload_from_string(csv_data, content_type='text/csv')
+    return {"message": "Data cleared successfully"}
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8123)

ow-stu-us-ce1-dev.json

@@ -0,0 +1,13 @@
+{
+  "type": "service_account",
+  "project_id": "ow-stu-us-ce1-dev",
+  "private_key_id": "1a9e8df697e5d4fbc088971f6eff64818b4b3361",
+  "private_key": "-----BEGIN PRIVATE KEY-----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\nwpumRo/yxJKI/bTC/83mmFA=\n-----END PRIVATE KEY-----\n",
+  "client_email": "gbucket@ow-stu-us-ce1-dev.iam.gserviceaccount.com",
+  "client_id": "109031272746562148979",
+  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+  "token_uri": "https://oauth2.googleapis.com/token",
+  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gbucket%40ow-stu-us-ce1-dev.iam.gserviceaccount.com",
+  "universe_domain": "googleapis.com"
+}

requirements.txt

@@ -2,4 +2,5 @@ fastapi
 uvicorn
 pandas
 gradio
-sqlalchemy
+sqlalchemy
+google-cloud-storage