Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import re
+from config import MODEL_PATH, ENCODER_DIR, OPENAI_API_KEY, OPENAI_BASE_URL
+from utils import load_model, load_label_encoders
+from prediction import predict_susceptibility
+from ai_assistant import initialize_openai_client, get_ai_response
+
+# Load assets
+model = load_model(MODEL_PATH)
+encoders = load_label_encoders(ENCODER_DIR)
+
+# Initialize OpenAI client
+client = initialize_openai_client(OPENAI_API_KEY, OPENAI_BASE_URL)
+
+# Streamlit UI
+st.set_page_config(page_title="Microbial Susceptibility Analyzer", layout="wide")
+
+st.sidebar.title("Navigation")
+page = st.sidebar.radio("Go to", ["Home", "Susceptibility Analysis", "Data Upload", "About"])
+
+# Home Page
+if page == "Home":
+    st.title("Microbial Susceptibility Analyzer")
+    st.image("bacteria.jpeg", use_container_width=True)
+    st.markdown("""
+    **Welcome to the Microbial Susceptibility Analyzer!**  
+    This app helps analyze **antibiotic resistance** using **machine learning and rule-based decisions**.
+    - Predict microbial susceptibility.
+    - Ask an AI assistant for expert advice.
+    - Upload datasets for batch predictions.
+    """)
+
+# Susceptibility Analysis Page
+elif page == "Susceptibility Analysis":
+    st.title("Susceptibility Prediction")
+    
+    # Initialize session state for messages if not exists
+    if 'messages' not in st.session_state:
+        st.session_state.messages = []
+    
+    # Create two columns for layout
+    col1, col2 = st.columns([1, 1])
+    
+    with col1:
+        with st.form("prediction_form"):
+            organism = st.selectbox('Organism', options=encoders['organism'].keys())
+            antibiotic = st.selectbox('Antibiotic', options=encoders['antibiotic'].keys())
+            was_positive = st.selectbox('Was Positive', options=[1, 0])
+            submit_button = st.form_submit_button("Predict")
+        
+        if submit_button:
+            # Store inputs in session state
+            st.session_state['current_organism'] = organism
+            st.session_state['current_antibiotic'] = antibiotic
+            st.session_state['current_was_positive'] = was_positive
+            
+            result = predict_susceptibility({
+                'was_positive': was_positive,
+                'organism': organism,
+                'antibiotic': antibiotic
+            }, model, encoders)
+            
+            st.subheader("Prediction Results")
+            if "Error" in result:
+                st.error(result["Error"])
+            else:
+                st.write(f"**Final Decision:** {result['Final Output']}")
+                st.write(f"**Rule-Based Guidance:** {result['Rule Guidance']}")
+                st.write(f"**Model Prediction:** {result['Model Prediction']}")
+                st.write(f"**Decision Explanation:** {result['Decision Reason']}")
+                
+                # Clear previous messages when new prediction is made
+                st.session_state.messages = []
+    
+    with col2:
+        st.subheader("DeepSeek AI Assistant")
+        
+        # Only show assistant if a prediction has been made
+        if 'current_organism' in st.session_state:
+            st.markdown(f"Ask about **{st.session_state.get('current_organism')}** and **{st.session_state.get('current_antibiotic')}**:")
+            
+            # Example prompts as buttons
+            example_prompts = [
+                "Explain why this combination might show resistance",
+                "Suggest alternative antibiotics for this organism",
+                "What resistance mechanisms are common here?",
+                "How should this result influence treatment decisions?"
+            ]
+            
+            # Create a unique key for each button
+            for i, prompt in enumerate(example_prompts):
+                if st.button(prompt, key=f"prompt_{i}"):
+                    # Create context-enhanced prompt
+                    enhanced_prompt = f"For organism {st.session_state.get('current_organism')} " \
+                                     f"and antibiotic {st.session_state.get('current_antibiotic')}: {prompt}"
+                    response = get_ai_response(client, enhanced_prompt)
+                    
+                    # Display the user prompt and AI response
+                    st.chat_message("user").markdown(prompt)
+                    st.chat_message("assistant").markdown(response)
+            
+            # Chat input
+            user_prompt = st.chat_input("Ask about this result...")
+            if user_prompt:
+                # Create context-enhanced prompt
+                enhanced_prompt = f"For organism {st.session_state.get('current_organism')} " \
+                                 f"and antibiotic {st.session_state.get('current_antibiotic')}: {user_prompt}"
+                response = get_ai_response(client, enhanced_prompt)
+                
+                # Display the user prompt and AI response
+                st.chat_message("user").markdown(user_prompt)
+                st.chat_message("assistant").markdown(response)
+        else:
+            st.info("Make a prediction first to get specific AI assistance")
+
+# Data Upload Page
+elif page == "Data Upload":
+    st.title("Batch Prediction: Upload CSV")
+    uploaded_file = st.file_uploader("Upload your CSV file", type=["csv"])
+    
+    if uploaded_file:
+        df = pd.read_csv(uploaded_file)
+        st.write("Uploaded Data Preview:", df.head())
+        
+        if st.button("Predict for Dataset"):
+            df["Prediction"] = df.apply(lambda row: predict_susceptibility(row.to_dict(), model, encoders)["Final Output"], axis=1)
+            st.write("Prediction Results", df)
+
+# About Page
+elif page == "About":
+    st.title("About this App")
+    st.markdown("""
+    - Developed by **Anulunko Chukwuebuka**  
+    - Uses **Machine Learning & Rule-based AI**  
+    - Integrated with **DeepSeek AI** for advanced queries  
+    - Contact: [email protected]  
+    """)