Update app.py

app.py

@@ -1,52 +1,52 @@
-import streamlit as st
-from transformers import pipeline
-
-# Load your trained model and tokenizer
-model_path = "C:\Clinical_Decesion_Support\clinical_decision_support_model"  # Update with your model path
-classifier = pipeline("text-classification", model=model_path)
-
-# Define the Streamlit app
-st.title("Clinical Decision Support System")
-st.write("Provide patient details to get a medical recommendation.")
-
-# Input fields for user
-age = st.number_input("Age", min_value=0, max_value=120, step=1, value=50)
-gender = st.selectbox("Gender", options=["Male", "Female"])
-weight = st.number_input("Weight (kg)", min_value=0, max_value=300, step=1, value=70)
-smoking_status = st.selectbox("Smoking Status", options=["Never", "Former", "Current"])
-diabetes = st.selectbox("Diabetes", options=["No", "Yes"])
-hypertension = st.selectbox("Hypertension", options=["No", "Yes"])
-cholesterol = st.number_input("Cholesterol (mg/dL)", min_value=0, max_value=500, step=1, value=200)
-heart_disease_history = st.selectbox("Heart Disease History", options=["No", "Yes"])
-symptoms = st.text_input("Symptoms", value="Chest pain")
-risk_score = st.number_input("Risk Score", min_value=0.0, max_value=10.0, step=0.1, value=5.0)
-
-# Button to get recommendation
-if st.button("Get Recommendation"):
-    # Convert inputs to model format
-    input_text = (
-        f"Age: {age}, Gender: {gender}, Weight: {weight}, Smoking Status: {smoking_status}, "
-        f"Diabetes: {1 if diabetes == 'Yes' else 0}, Hypertension: {1 if hypertension == 'Yes' else 0}, "
-        f"Cholesterol: {cholesterol}, Heart Disease History: {1 if heart_disease_history == 'Yes' else 0}, "
-        f"Symptoms: {symptoms}, Risk Score: {risk_score}"
-    )
-
-    # Get prediction from the model
-    prediction = classifier(input_text)
-    recommendation_label = prediction[0]['label']
-    
-    # Map label to recommendation title (ensure you have reverse mapping loaded)
-    reverse_label_mapping = {
-        "LABEL_0": "Maintain healthy lifestyle",
-        "LABEL_1": "Immediate cardiologist consultation",
-        "LABEL_2": "Start statins, monitor regularly",
-        "LABEL_3": "Lifestyle changes, monitor",
-        "LABEL_4": "No immediate action",
-        "LABEL_5": "Increase statins, lifestyle changes",
-        "LABEL_6": "Start ACE inhibitors, monitor"
-    }
-    recommendation = reverse_label_mapping.get(recommendation_label, "Unknown Recommendation")
-    
-    # Display the recommendation
-    st.subheader("Recommendation")
-    st.write(recommendation)
+import streamlit as st
+from transformers import pipeline
+
+# Load your trained model and tokenizer
+model_path = "quadranttechnologies/Clinical_Decision_Support"  # Update with your model path
+classifier = pipeline("text-classification", model=model_path)
+
+# Define the Streamlit app
+st.title("Clinical Decision Support System")
+st.write("Provide patient details to get a medical recommendation.")
+
+# Input fields for user
+age = st.number_input("Age", min_value=0, max_value=120, step=1, value=50)
+gender = st.selectbox("Gender", options=["Male", "Female"])
+weight = st.number_input("Weight (kg)", min_value=0, max_value=300, step=1, value=70)
+smoking_status = st.selectbox("Smoking Status", options=["Never", "Former", "Current"])
+diabetes = st.selectbox("Diabetes", options=["No", "Yes"])
+hypertension = st.selectbox("Hypertension", options=["No", "Yes"])
+cholesterol = st.number_input("Cholesterol (mg/dL)", min_value=0, max_value=500, step=1, value=200)
+heart_disease_history = st.selectbox("Heart Disease History", options=["No", "Yes"])
+symptoms = st.text_input("Symptoms", value="Chest pain")
+risk_score = st.number_input("Risk Score", min_value=0.0, max_value=10.0, step=0.1, value=5.0)
+
+# Button to get recommendation
+if st.button("Get Recommendation"):
+    # Convert inputs to model format
+    input_text = (
+        f"Age: {age}, Gender: {gender}, Weight: {weight}, Smoking Status: {smoking_status}, "
+        f"Diabetes: {1 if diabetes == 'Yes' else 0}, Hypertension: {1 if hypertension == 'Yes' else 0}, "
+        f"Cholesterol: {cholesterol}, Heart Disease History: {1 if heart_disease_history == 'Yes' else 0}, "
+        f"Symptoms: {symptoms}, Risk Score: {risk_score}"
+    )
+
+    # Get prediction from the model
+    prediction = classifier(input_text)
+    recommendation_label = prediction[0]['label']
+    
+    # Map label to recommendation title (ensure you have reverse mapping loaded)
+    reverse_label_mapping = {
+        "LABEL_0": "Maintain healthy lifestyle",
+        "LABEL_1": "Immediate cardiologist consultation",
+        "LABEL_2": "Start statins, monitor regularly",
+        "LABEL_3": "Lifestyle changes, monitor",
+        "LABEL_4": "No immediate action",
+        "LABEL_5": "Increase statins, lifestyle changes",
+        "LABEL_6": "Start ACE inhibitors, monitor"
+    }
+    recommendation = reverse_label_mapping.get(recommendation_label, "Unknown Recommendation")
+    
+    # Display the recommendation
+    st.subheader("Recommendation")
+    st.write(recommendation)