Update app.py

app.py

@@ -1,74 +1,107 @@
-import os
-import streamlit as st
-from groq import Groq
+import gradio as gr
+from tensorflow.keras.models import load_model
+import numpy as np
+from PIL import Image
 
-# ✅ Fix TensorFlow CPU warnings
-os.environ["TF_ENABLE_ONEDNN_OPTS"] = "0"
+# Load model
+model = load_model('xray_image_classifier_model.keras')
 
-# ✅ Set up the Groq API Key
-GROQ_API_KEY = "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"
-os.environ["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."
+}
 
-# ✅ Initialize the Groq client (Fixed 'proxies' issue)
-client = Groq(api_key=GROQ_API_KEY)
+# Prediction function
+def predict(image):
+    img = image.resize((150, 150))
+    img_array = np.array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+    
+    prediction = model.predict(img_array)
+    predicted_class = "Pneumonia" if prediction > 0.5 else "Normal"
+    
+    # Get the corresponding solution
+    solution = solutions.get(predicted_class, "No specific advice available.")
+    
+    return predicted_class, solution
 
-# ✅ Streamlit UI setup
-st.set_page_config(page_title="AI Disease Detection Assistant", page_icon="🩺", layout="wide")
-st.title("🩺 AI Disease Detection Chatbot")
-st.write("Hello! I'm your AI assistant for disease-related queries. Ask me about symptoms, treatments, or general health advice.")
+# CSS Styling
+css = """
+    .gradio-container {
+        background-color: #f5f5f5;
+        font-family: Arial, sans-serif;
+    }
+    
+    .gr-button {
+        background-color:#007bff;
+        color: white; 
+        border: none;
+        border-radius: 5px;
+        font-size: 16px;
+        padding: 10px 20px;
+        cursor: pointer;
+        transition: background-color 0.3s ease;
+    }
 
-# ✅ Sidebar settings
-st.sidebar.header("⚙️ Settings")
-chat_theme = st.sidebar.radio("Choose a theme:", ["Light", "Dark", "Blue", "Green"])
+    .gr-button:hover {
+        background-color: #0056b3;
+    }
 
-# ✅ Apply themes
-themes = {
-    "Dark": "#1e1e1e",
-    "Blue": "#e3f2fd",
-    "Green": "#e8f5e9",
-    "Light": "#ffffff"
-}
-st.markdown(f"""
-    <style>
-        body {{ background-color: {themes[chat_theme]}; color: black; }}
-        .stButton>button {{ background-color: #4CAF50; color: white; }}
-        .chat-bubble {{ background-color: #f1f1f1; border-radius: 10px; padding: 10px; }}
-    </style>
-""", unsafe_allow_html=True)
+    .gr-textbox, .gr-image {
+        border: 2px dashed #007bff;
+        padding: 20px;
+        border-radius: 10px;
+        background-color: #ffffff;
+    }
 
-# ✅ Session state for chat history
-if 'conversation_history' not in st.session_state:
-    st.session_state.conversation_history = []
+    .gr-box-text {
+        color: #007bff;
+        font-size: 22px;
+        font-weight: bold;
+        text-align: center;
+    }
 
-# ✅ Function to generate AI response
-def generate_chatbot_response(user_message):
-    # Custom responses
-    if "who created you" in user_message.lower():
-        return "I was created by Abdel Basit. 😊"
+    h1 {
+        font-size: 36px;
+        color: #007bff;
+        text-align: center;
+    }
 
-    # AI model response
-    prompt = f"You are a medical AI assistant. The user asks: {user_message}. Provide a detailed, accurate medical response."
-    
-    try:
-        chat_completion = client.chat.completions.create(
-            messages=[{"role": "user", "content": prompt}],
-            model="llama3-8b-8192"
-        )
-        return chat_completion.choices[0].message.content
-    except Exception as e:
-        return f"⚠️ Error: {str(e)}"
+    p {
+        font-size: 20px;
+        color: #333;
+        text-align: center;
+    }
+"""
+
+# 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.
 
-# ✅ User chat input
-st.markdown("### 💬 Chat with me")
-user_input = st.chat_input("Ask me a health-related question:")
+**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
+"""
 
-if user_input:
-    chatbot_response = generate_chatbot_response(user_input)
-    st.session_state.conversation_history.append(("User: " + user_input, "Chatbot: " + chatbot_response))
+# Gradio UI
+with gr.Blocks(css=css) as interface:
+    gr.Markdown("<h1>Automated Pneumonia Detection via Chest X-ray Classification</h1>")
+    gr.Markdown("<p>Submit a chest X-ray image below.</p>")
+    
+    with gr.Row():
+        image_input = gr.Image(label="Drop Image Here", type="pil", elem_classes=["gr-image", "gr-box-text"])
+        output_prediction = gr.Textbox(label="Model Analysis Output", elem_classes=["gr-textbox", "gr-box-text"])
+        output_solution = gr.Textbox(label="Recommended Solution", elem_classes=["gr-textbox", "gr-box-text"])
+
+    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)
 
-# ✅ Display chat history
-st.markdown("---")
-st.markdown("### 🗨️ Chat History")
-for question, answer in st.session_state.conversation_history:
-    st.write(f"<div class='chat-bubble'><b>{question}</b></div>", unsafe_allow_html=True)
-    st.write(f"<div class='chat-bubble'>{answer}</div>", unsafe_allow_html=True)
+# Launch the app
+interface.launch()