--- a
+++ b/main.py
@@ -0,0 +1,97 @@
+import joblib
+from fastapi import FastAPI, Request, Form
+from fastapi.responses import HTMLResponse
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
+from pydantic import BaseModel
+import uvicorn
+# Load the saved model
+model = joblib.load('cancer_model.pkl')
+import sklearn
+print(sklearn.__version__)
+# Define the input data schema
+class PredictionInput(BaseModel):
+    gender: str = Form()
+    age: int = Form()
+    smoking: str = Form()
+    yellowFingers: str = Form()
+    anxiety: str = Form()
+    peerPressure: str = Form()
+    chronicDisease: str = Form()
+    fatigue: str = Form()
+    allergy: str = Form()
+    wheezing: str = Form()
+    alcoholConsuming: str = Form()
+    coughing: str = Form()
+    shortnessOfBreath: str = Form()
+    swallowingDifficulty: str = Form()
+    chestPain: str = Form()
+    
+    
+# Create the FastAPI application
+app = FastAPI()
+templates = Jinja2Templates(directory="static")
+
+# Mount the static files directory to serve the HTML and CSS files
+app.mount("/static", StaticFiles(directory="static"), name="static")
+
+
+# Define the prediction route
+@app.get("/", response_class=HTMLResponse)
+async def home(request: Request):
+    return templates.TemplateResponse("/index.html", {"request": request})
+
+
+@app.post("/predict")
+async def predict_lung_cancer(
+    request: Request,
+    gender: str = Form(),
+    age: int = Form(),
+    smoking: str = Form(),
+    yellowFingers: str = Form(),
+    anxiety: str = Form(),
+    peerPressure: str = Form(),
+    chronicDisease: str = Form(),
+    fatigue: str = Form(),
+    allergy: str = Form(),
+    wheezing: str = Form(),
+    alcoholConsuming: str = Form(),
+    coughing: str = Form(),
+    shortnessOfBreath: str = Form(),
+    swallowingDifficulty: str = Form(),
+    chestPain: str = Form()
+):
+    # Convert the input data to a dictionary
+    
+    input_data = {
+        # "gender":gender,
+        # "age":float(age),
+        # "smoking":float(smoking),
+        "yellowFingers":float(yellowFingers),
+        "anxiety":float(anxiety),
+        "peerPressure":float(peerPressure),
+        "chronicDisease":float(chronicDisease),
+        "fatigue":float(fatigue),
+        "allergy":float(allergy),
+        "wheezing":float(wheezing),
+        "alcoholConsuming":float(alcoholConsuming),
+        "coughing":float(coughing),
+        "shortnessOfBreath":float(shortnessOfBreath),
+        "swallowingDifficulty":float(swallowingDifficulty),
+        "chestPain":float(chestPain)
+    }
+    
+    print(type(input_data))
+    
+    # Prepare the input features for prediction
+    input_features = [input_data[col] for col in input_data]
+    
+    # Make the prediction
+    prediction = model.predict([input_features])[0]
+    
+
+    
+    # Return the prediction as a JSON response
+    return {"prediction": int(prediction)}
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8000)
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