Initial files

Dockerfile

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+# pull python base image
+FROM python:3.10
+
+COPY requirements.txt requirements.txt
+
+# update pip
+RUN pip install --upgrade pip
+
+# install dependencies
+RUN pip install -r requirements.txt
+
+RUN useradd -m -u 1000 myuser
+
+USER myuser
+
+# copy application files
+COPY --chown=myuser . .
+
+# expose port for application
+EXPOSE 8001
+
+# start fastapi application
+CMD ["python", "app.py"]

README.md

@@ -6,6 +6,7 @@ colorTo: blue
 sdk: docker
 pinned: false
 license: mit
+app_port: 8001
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+from fastapi import FastAPI, Request, Response
+
+import gradio
+from transformers import pipeline
+
+from sklearn.metrics import f1_score, precision_score, recall_score
+import prometheus_client as prom
+
+
+app = FastAPI()
+
+username = "runaksh"
+repo_name = "finetuned-sentiment-model"
+repo_path = username+ '/' + repo_name
+sentiment_model = pipeline(model= repo_path)
+
+import pandas as pd
+
+test_data = pd.read_csv("test_reviews.csv")
+
+f1_metric = prom.Gauge('sentiment_f1_score', 'F1 score for random 100 test samples')
+precision_metric = prom.Gauge('sentiment_precision_score', 'Precision score for random 100 test samples')
+recall_metric = prom.Gauge('sentiment_recall_score', 'Recall score for random 100 test samples')
+
+
+# Function for response generation
+def predict_sentiment(text):
+    result = sentiment_model(text)
+    if result[0]['label'].endswith('0'):
+        return 'Negative'
+    else:
+        return 'Positive'
+
+# Function for updating metrics
+def update_metrics():
+    test = test_data.sample(100)
+    test_text = test['Text'].values
+    test_pred = sentiment_model(list(test_text))
+    pred_labels = [int(pred['label'].split("_")[1]) for pred in test_pred]
+    f1 = f1_score(test['labels'], pred_labels).round(3)
+    precision = precision_score(test['labels'], pred_labels).round(3)
+    recall = recall_score(test['labels'], pred_labels).round(3)
+    
+    f1_metric.set(f1)
+    precision_metric.set(precision)
+    recall_metric.set(recall)
+
+
+@app.get("/metrics")
+async def get_metrics():
+    update_metrics()
+    return Response(media_type="text/plain", content= prom.generate_latest())
+
+
+# Input from user
+in_prompt = gradio.components.Textbox(lines=10, placeholder=None, label='Enter review text')
+
+# Output response
+out_response = gradio.components.Textbox(type="text", label='Sentiment')
+
+# Gradio interface to generate UI link
+title = "Sentiment Classification"
+description = "Analyse sentiment of the given review"
+
+iface = gradio.Interface(fn = predict_sentiment,
+                         inputs = [in_prompt],
+                         outputs = [out_response],
+                         title = title,
+                         description = description)
+
+app = gradio.mount_gradio_app(app, iface, path="/")
+
+#iface.launch(server_name = "0.0.0.0", server_port = 8001) # Ref. for parameters: https://www.gradio.app/docs/interface
+
+if __name__ == "__main__":
+    # Use this for debugging purposes only
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8001)

flagged/samplefile.txt

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+Sample file

requirements.txt

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+torch
+gradio
+transformers
+
+uvicorn
+fastapi
+python-multipart
+pydantic
+
+scikit-learn
+prometheus-client