Deploy Streamlit app

app.py

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+import streamlit as st
+from groq import Groq
+import base64
+from PIL import Image
+import io
+import os
+from dotenv import load_dotenv
+from io import BytesIO
+import requests
+import streamlit.components.v1 as components
+
+st.set_page_config(page_title="My App", page_icon="", layout="wide", initial_sidebar_state="collapsed")
+import streamlit as st
+
+# Initialize session state for dark mode (default: True)
+if "dark_mode" not in st.session_state:
+    st.session_state.dark_mode = True
+
+# Icon switch
+icon = "☀️" if st.session_state.dark_mode else "🌙"
+
+# Toggle button (tap to switch modes)
+if st.button(icon, key="dark_mode_toggle"):
+    st.session_state.dark_mode = not st.session_state.dark_mode
+    st.rerun()
+
+# Apply styles for dark & light modes
+if st.session_state.dark_mode:
+    st.markdown(
+        """
+        <style>
+        body, .stApp { background-color: #121212; color: white; }
+        h1, h2, h3, p, label { color: white !important; }
+        .stTextInput, .stButton > button, .stSelectbox, .stTextArea {
+            background-color: #222; 
+            color: white; 
+            border-radius: 10px;
+            border: 1px solid #444;
+        }
+        .stMarkdown a { color: #4db8ff; }
+        .stAlert { background-color: #222; color: white; }
+        </style>
+        """,
+        unsafe_allow_html=True,
+    )
+else:
+    st.markdown(
+        """
+        <style>
+        body, .stApp { background-color: #ffffff; color: #333; }
+        h1, h2, h3, p, label { color: #333 !important; }
+        .stTextInput, .stButton > button, .stSelectbox, .stTextArea {
+            background-color: #f8f9fa; 
+            color: #333; 
+            border-radius: 10px; 
+            border: 1px solid #ccc;
+        }
+        .stMarkdown a { color: #007bff; }
+        .stAlert { background-color: #f8f9fa; color: #333; }
+        </style>
+        """,
+        unsafe_allow_html=True,
+    )
+
+
+# Hides Streamlit UI elements with CSS
+hide_streamlit_style = """
+    <style>
+        #MainMenu {visibility: hidden;}
+        footer {visibility: hidden;}
+        header {visibility: hidden;}
+        .stDeployButton {display: none !important;} 
+        [data-testid="stProfileMenu"] {display: none !important;}  
+    </style>
+"""
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)
+
+
+
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Retrieve Groq API key from environment variables
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+
+# Check if API key is loaded correctly
+if not GROQ_API_KEY:
+    st.error("Groq API key not found. Please set it in the .env file.")
+    st.stop()
+
+# Initialize Groq client
+client = Groq(api_key=GROQ_API_KEY)
+
+def encode_image(image):
+    """Convert a PIL Image object to base64-encoded JPEG format"""
+    # Convert RGBA to RGB if needed
+    if image.mode == "RGBA":
+        image = image.convert("RGB")
+
+    # Save image as JPEG in memory
+    buffer = BytesIO()
+    image.save(buffer, format="JPEG")
+    base64_image = base64.b64encode(buffer.getvalue()).decode("utf-8")
+    
+    return base64_image
+
+# Function to classify MRI image using Groq API
+def classify_mri_image(image):
+    base64_image = encode_image(image)
+    
+    # Prompt for the Groq API
+    prompt = "Analyze this MRI image and determine if it shows a brain tumor. Provide a clear classification (e.g., 'Tumor detected' or 'No tumor detected') and a brief explanation."
+    
+    # Call Groq API with Llama 3.2-90B Vision Preview
+    response = client.chat.completions.create(
+        model="llama-3.2-90b-vision-preview",
+        messages=[
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": prompt},
+                    {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}}
+                ]
+            }
+        ],
+        max_tokens=300
+    )
+    
+    # Extract the response
+    result = response.choices[0].message.content
+    return result
+
+# Streamlit app
+def main():
+    st.title("MRI Brain Tumor Classifier")
+    st.write("Upload an MRI image to classify whether it contains a brain tumor.")
+
+    uploaded_file = st.file_uploader("Choose an MRI image...", type=["jpg", "jpeg", "png"])
+    
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.image(image, caption="Uploaded MRI Image", use_container_width=True)
+
+        if st.button("Classify Image"):
+            with st.spinner("Classifying..."):
+                try:
+                    result = classify_mri_image(image)
+                    st.success("Classification Complete!")
+                    st.write("### Result:")
+                    st.write(result)
+                except Exception as e:
+                    st.error(f"An error occurred: {str(e)}")
+
+if __name__ == "__main__":
+    main()
+
+
+
+st.markdown("---")
+
+
+# Function to check if the question is related to brain tumors
+def is_brain_tumor_question(user_input):
+    keywords = [
+    "brain tumor", "glioma", "meningioma", "astrocytoma", "medulloblastoma", 
+    "ependymoma", "oligodendroglioma", "pituitary tumor", "schwannoma", "craniopharyngioma",
+    "cancer", "brain cancer", "malignant tumor", "benign tumor", 
+    "neurology", "oncology", "neurosurgeon", "brain MRI", "CT scan brain", "tumor diagnosis",
+    "tumor treatment", "chemotherapy", "radiotherapy", "stereotactic radiosurgery", 
+    "brain surgery", "craniotomy", "tumor removal", "brain biopsy",
+    "symptoms of brain tumor", "headache and tumor", "seizures and tumor", 
+    "brain tumor prognosis", "life expectancy brain tumor", "brain metastases",
+    "tumor recurrence", "brain swelling", "intracranial pressure", "glioblastoma multiforme",
+    "brain tumor in children", "brain tumor in adults", "radiation therapy for brain tumors", "brain",
+    "brain-tumor"
+]
+    return any(keyword in user_input.lower() for keyword in keywords)
+
+# Function to interact with Groq chatbot
+def get_chatbot_response(user_input):
+    if not is_brain_tumor_question(user_input):
+        return "I can only answer brain tumor-related questions."
+
+    url = "https://api.groq.com/openai/v1/chat/completions"
+    headers = {
+        "Authorization": f"Bearer {GROQ_API_KEY}",
+        "Content-Type": "application/json"
+    }
+    payload = {
+        "model": "llama3-8b-8192",
+        "messages": [
+            {"role": "system", "content": "You are a helpful AI that answers only brain tumor-related questions. Keep responses a little bit brief if question doesn't demand explanations."},
+            {"role": "user", "content": user_input}
+        ]
+    }
+    
+    try:
+        response = requests.post(url, headers=headers, json=payload)
+        response_data = response.json()
+        if response.status_code == 200:
+            return response_data.get("choices", [{}])[0].get("message", {}).get("content", "No response generated.")
+        else:
+            return f"Error {response.status_code}: {response_data.get('error', {}).get('message', 'Unknown error')}"
+    except requests.exceptions.RequestException as e:
+        return f"Request failed: {e}"
+
+# Custom CSS for styling the input and send icon
+import streamlit as st
+
+st.markdown("""
+    <style>
+    .chat-container {
+        display: flex;
+        flex-direction: column;
+        align-items: center;
+        border: 1px solid #444;
+        border-radius: 20px;
+        padding: 10px;
+        background-color: #222;
+        width: 100%;
+        max-width: 400px;
+        margin: auto;
+    }
+    .chat-input {
+        height: 45px;
+        font-size: 17px; /* Slightly increased */
+        border: none;
+        width: 100%;
+        max-width: 400px;
+        border-radius: 10px;
+        padding: 10px;
+        background-color: #333;
+        color: white;
+        outline: none;
+    }
+    .send-button {
+        cursor: pointer;
+        font-weight: bold;
+        text-align: center;
+         background-color: #111f3f;
+        color: white;
+        transition: background 0.3s ease-in-out;
+        margin-top: 10px;
+        border: none;
+        border-radius: 10px;
+        width: 100%;
+        max-width: 400px;
+        height: 45px;
+        padding: 12px;
+        font-size: 17px; /* Slightly increased */
+    }
+    .send-button:hover {
+        background-color: #888888; /* Slightly darker gray on hover */
+    }
+
+    /* Mobile View */
+    @media (max-width: 600px) {
+        .chat-container {
+            width: 90%;
+        }
+        .chat-input {
+            width: 100%;
+            font-size: 16px; /* Slightly increased */
+            height: 40px;
+            padding: 8px;
+        }
+        .send-button {
+            width: 128px; /* 2 inches */
+            font-size: 16px; /* Slightly increased */
+            height: 40px;
+            padding: 8px;
+        }
+    }
+</style>
+
+""", unsafe_allow_html=True)
+
+st.write("Ask me any brain tumor-related questions")
+
+user_input = st.text_input("", placeholder="Enter your question...", key="input_box", label_visibility="collapsed")
+
+st.markdown("""  
+    <div style="display: flex; justify-content: center; width: 100%; margin-top: 10px;">
+        <button class="send-button" onclick="sendMessage()">Send</button>
+    </div>
+""", unsafe_allow_html=True)
+
+if user_input:
+    st.write("")  # Adds a space before generating the response
+    with st.spinner("Thinking..."):
+        response = get_chatbot_response(user_input)
+    st.write(response)
+

requirements.txt

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+streamlit
+tensorflow-cpu
+numpy
+matplotlib
+Pillow
+opencv-python-headless
+requests
+python-dotenv
+groq