Update app.py

app.py

@@ -1,18 +1,28 @@
+import os
 import gradio as gr
-from tensorflow.keras.models import load_model
+import streamlit as st
+from groq import Groq
 import numpy as np
 from PIL import Image
+from tensorflow.keras.models import load_model
 
-# Load model
+# Load Pneumonia Detection Model
 model = load_model('xray_image_classifier_model.keras')
 
+# Set up Groq API Key
+GROQ_API_KEY = "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"
+os.environ["GROQ_API_KEY"] = GROQ_API_KEY
+
+# Initialize the Groq client
+client = Groq(api_key=GROQ_API_KEY)
+
 # Define solutions
 solutions = {
     "Pneumonia": "Consult a doctor immediately. Follow prescribed antibiotics if given, rest well, and stay hydrated.",
     "Normal": "Your X-ray appears normal. However, if you experience symptoms, consult a doctor for further evaluation."
 }
 
-# Prediction function
+# Prediction Function
 def predict(image):
     img = image.resize((150, 150))
     img_array = np.array(img) / 255.0
@@ -26,7 +36,7 @@ def predict(image):
     
     return predicted_class, solution
 
-# CSS Styling
+# CSS Styling for Gradio
 css = """
     .gradio-container {
         background-color: #f5f5f5;
@@ -75,21 +85,8 @@ css = """
     }
 """
 
-# Description
-description = """
-**Automated Pneumonia Detection via Chest X-ray Classification**
-
-This model leverages deep learning techniques to classify chest X-ray images as either 'Pneumonia' or 'Normal.' By utilizing the InceptionV3 architecture for transfer learning, combined with data preprocessing and augmentation, the model aims to deliver powerful performance in medical image analysis. It enhances the automation of diagnostic processes, aiding in the detection of pneumonia with high accuracy.
-
-**Technologies Employed:**
-- TensorFlow & Keras for model development
-- InceptionV3 for transfer learning
-- Numpy, Pandas, and Matplotlib for data handling and visualization
-- Flask and Gradio for deployment and user interaction
-"""
-
-# Gradio UI
-with gr.Blocks(css=css) as interface:
+# Gradio UI for Pneumonia Detection
+with gr.Blocks(css=css) as gradio_interface:
     gr.Markdown("<h1>Automated Pneumonia Detection via Chest X-ray Classification</h1>")
     gr.Markdown("<p>Submit a chest X-ray image below.</p>")
     
@@ -101,7 +98,68 @@ with gr.Blocks(css=css) as interface:
     submit_btn = gr.Button("Initiate Diagnostic Analysis", elem_classes=["gr-button"])
     submit_btn.click(fn=predict, inputs=image_input, outputs=[output_prediction, output_solution])
     
-    gr.Markdown(description)
+    gr.Markdown("<h3>Note:</h3> <p>The AI model provides an initial assessment. Always consult a doctor for final diagnosis.</p>")
+
+# Streamlit UI for Disease Chatbot
+st.set_page_config(page_title="AI Health Assistant", page_icon="🩺", layout="wide")
+st.title("🩺 AI Health Assistant")
+st.write("Welcome! Upload an X-ray for pneumonia detection or ask the chatbot about diseases.")
+
+# Sidebar Theme Settings
+st.sidebar.header("⚙️ Settings")
+chat_theme = st.sidebar.radio("Choose a theme:", ["Light", "Dark", "Blue", "Green"])
+
+if chat_theme == "Dark":
+    st.markdown("""
+        <style>
+            body {background-color: #1e1e1e; color: white;}
+            .stButton>button {background-color: #4CAF50; color: white;}
+            .chat-bubble {background-color: #2c2c2c; border-radius: 10px; padding: 10px;}
+        </style>
+    """, unsafe_allow_html=True)
+elif chat_theme == "Blue":
+    st.markdown("""
+        <style>
+            body {background-color: #e3f2fd; color: black;}
+            .stButton>button {background-color: #2196F3; color: white;}
+            .chat-bubble {background-color: #bbdefb; border-radius: 10px; padding: 10px;}
+        </style>
+    """, unsafe_allow_html=True)
+elif chat_theme == "Green":
+    st.markdown("""
+        <style>
+            body {background-color: #e8f5e9; color: black;}
+            .stButton>button {background-color: #4CAF50; color: white;}
+            .chat-bubble {background-color: #c8e6c9; border-radius: 10px; padding: 10px;}
+        </style>
+    """, unsafe_allow_html=True)
+
+# Chatbot Function
+def generate_chatbot_response(user_message):
+    if "who created you" in user_message.lower():
+        return "I was created by Abdel Basit. 😊"
+    
+    prompt = f"You are a helpful AI chatbot for medical guidance. The user is asking: {user_message}. Provide a detailed, professional response."
+    
+    chat_completion = client.chat.completions.create(
+        messages=[{"role": "user", "content": prompt}],
+        model="llama3-8b-8192",
+    )
+    
+    return chat_completion.choices[0].message.content
+
+# Chatbot Interface
+st.markdown("### 💬 Chat with the AI Health Assistant")
+user_input = st.chat_input("Ask me a health-related question:")
+
+if user_input:
+    chatbot_response = generate_chatbot_response(user_input)
+    st.markdown(f"**You:** {user_input}")
+    st.markdown(f"**AI:** {chatbot_response}")
+
+# Launch Gradio Interface in Streamlit
+st.markdown("---")
+st.markdown("## 🔬 Pneumonia Detection System")
+with st.expander("Click here to open the AI Pneumonia Detection System"):
+    gradio_interface.launch()
 
-# Launch the app
-interface.launch()