All files are added

Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+# expose FastAPI on port 8000
+EXPOSE 8000
+# expose Streamlit on port 8501
+EXPOSE 8501
+
+# run both in parallel
+CMD ["sh", "-c", "uvicorn main:app --host 0.0.0.0 --port 8000 & streamlit run frontend.py --server.port=8501 --server.address=0.0.0.0"]

frontend.py

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+import streamlit as st 
+import requests
+
+API_URL='http://127.0.0.1:8000/prediction'
+
+st.title("Iris Flower Predictor")
+st.markdown("Enter the flower samples below")
+
+sepal_length = st.number_input('Sepal_length',min_value=0.1,max_value=10.0,value=4.0)
+sepal_width =st.number_input('sepal_width',min_value=0.1,max_value=10.1,value=5.0)
+petal_length = st.number_input('Petal_legth',max_value=10.1,min_value=0.1,value=5.0)
+petal_width = st.number_input('petal_width',max_value=10.1,min_value=0.1,value=4.0)
+
+if st.button('predict Flower class'):
+    input_data ={
+        'sepal_length':sepal_length,
+        'sepal_width': sepal_width,
+        'petal_length': petal_length,
+        'petal_width': petal_width
+    }
+
+    try:
+        response = requests.post(API_URL,json=input_data)
+        if response.status_code==200:
+            predicition =response.json()
+            st.success(f"prediction: {predicition['Predicted Class']}")
+
+        else:
+            st.error(f"Error: {response.status_code}-{response.txt}") 
+
+
+    except Exception as e:
+        st.error(f'An error occcurred:{e}')                     

main.py

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+from fastapi import FastAPI 
+import pickle
+from pydantic import BaseModel ,Field
+from typing import Annotated
+from fastapi.responses import JSONResponse
+
+with open('model.pkl','rb') as f :
+    model = pickle.load(f)
+
+
+class data_validation(BaseModel):
+    sepal_length : Annotated[float,Field(...,description='Enter the sepal length',examples=['0.1 to 10'],gt=0,le=10)]
+    sepal_width  : Annotated[float,Field(...,description='Enter the sepal width',examples=['0.1 to 10'],gt=0 ,le=10)]
+    petal_length : Annotated[float,Field(...,description='Enter the petal legth',examples=['0.1 to 10'],gt=0,le=10)]
+    petal_width : Annotated[float,Field(...,description='Enter the petal width',examples=['0.1 to 10'],gt=0,le=10)]
+
+
+
+
+app = FastAPI()
+
+
+@app.get("/")
+def start():
+    return {'message':'Welcome to iris classifier'}
+
+
+
+@app.post("/prediction")
+def prediction_by_model(data:data_validation):
+
+    input_data = [[
+        data.sepal_length,
+        data.sepal_width,
+        data.petal_length,
+        data.petal_width
+    ]]
+
+    prediction = model.predict(input_data)[0]
+    def prediction_class(prediction:prediction):
+        
+        if int(prediction)==0:
+            return 'ris setosa'
+        
+        elif int(prediction)==1:
+            return  'Iris virginica'
+        
+        elif int(prediction)==2:
+            return  'Iris versicolo'
+        
+        else:
+            return "unknow"
+    
+
+
+    return  JSONResponse(status_code=200,content={'Predicted Class':prediction_class(prediction)})
+    
+    
+
+
+

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:62ab9d2eab2b83e6afce108e988f275671ad9e3696aa351bb01b036db60dfef6
+size 178269