app.py

import streamlit as st
from transformers import AutoImageProcessor, AutoModelForImageClassification
from groq import Groq
import torch
from PIL import Image
from gtts import gTTS
import os
import re
import tempfile

# Set page config for a wide, user-friendly layout
st.set_page_config(page_title="Farmer's Friend", page_icon="🌱", layout="wide", initial_sidebar_state="expanded")

# Initialize Groq client using environment variable (set as a secret in Hugging Face Spaces)
groq_api_key = os.getenv("GROQ_API_KEY")
if not groq_api_key:
    st.error("Groq API key not found. Please set the GROQ_API_KEY environment variable in Hugging Face Spaces secrets.")
    st.stop()
groq_client = Groq(api_key=groq_api_key)

# Load and cache the model and processor
@st.cache_resource
def load_model_and_processor():
    image_processor = AutoImageProcessor.from_pretrained("wambugu71/crop_leaf_diseases_vit", use_fast=True)
    image_model = AutoModelForImageClassification.from_pretrained("wambugu71/crop_leaf_diseases_vit")
    image_model.eval()  # Set model to evaluation mode for faster inference
    if torch.cuda.is_available():
        image_model.cuda()  # Use GPU if available
    return image_processor, image_model

image_processor, image_model = load_model_and_processor()

# Check for wide-field image
def is_wide_field_image(image):
    width, height = image.size
    return width > 1000 or height > 1000

# Generate audio in English using temporary files
@st.cache_data
def text_to_audio(text, filename="output.mp3"):
    try:
        # Use a temporary file to store the audio
        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
            tts = gTTS(text=text, lang='en', slow=False, tld='com')
            tts.save(tmp_file.name)
            return tmp_file.name
    except Exception as e:
        st.error(f"Error generating audio: {e}")
        return None

# Smart voice-over generation (removing special characters)
@st.cache_data
def generate_voice_over(predicted_label, recommendation):
    clean_label = re.sub(r'[_*]', ' ', predicted_label).strip()
    if "healthy" in clean_label.lower():
        voice_text = "Your crop looks healthy. Keep up the good work!"
    else:
        voice_text = f"Issue detected: {clean_label}. Recommendation: {recommendation}"
    return text_to_audio(voice_text, "result.mp3")

# Analyze the crop (no caching due to PIL.Image.Image serialization issues)
def analyze_crop(image, crop_type):
    if image is None:
        msg = "Please upload a photo of your crop leaf."
        return msg, text_to_audio(msg, "no_image.mp3")

    if is_wide_field_image(image):
        msg = "This image is too large. Please upload a close-up photo of the leaf."
        return msg, text_to_audio(msg, "large_image.mp3")

    # Convert image to RGB and resize (optimized)
    image = image.convert("RGB")
    image = image.resize((224, 224), Image.Resampling.LANCZOS)
    inputs = image_processor(images=image, return_tensors="pt")

    # Move inputs to GPU if available
    if torch.cuda.is_available():
        inputs = {k: v.cuda() for k, v in inputs.items()}

    # Run inference
    with torch.no_grad():
        outputs = image_model(**inputs)
        predicted_class_idx = outputs.logits.argmax(-1).item()
        predicted_label = image_model.config.id2label[predicted_class_idx]

    # Prepare result
    if "healthy" in predicted_label.lower():
        issue = "Healthy"
        recommendation = "Your crop looks healthy. Keep up the good work!"
    else:
        issue = predicted_label
        try:
            prompt = f"Detected issue: {predicted_label} in {crop_type}. Suggest general remedies for this crop problem suitable for farmers worldwide in English."
            response = groq_client.chat.completions.create(
                model='llama3-8b-8192',
                messages=[{'role': 'user', 'content': prompt}],
                timeout=5  # Add timeout to avoid long waits
            ).choices[0].message.content
            recommendation = response
        except Exception as e:
            recommendation = "Sorry, there was an issue retrieving recommendations. Please consult a local agricultural expert."

    result_text = f"**Issue:** {issue}. **Recommendation:** {recommendation}"
    audio_file = generate_voice_over(predicted_label, recommendation)
    return result_text, audio_file

# Custom CSS for a Grok-inspired design
st.markdown("""
    <style>
        .stApp {
            background-color: #F5F7F5;
            font-family: 'Arial', sans-serif;
        }
        .main-header {
            color: #1A3C34;
            font-size: 40px;
            font-weight: 700;
            text-align: center;
            margin-bottom: 10px;
        }
        .sub-header {
            color: #2E7D32;
            font-size: 20px;
            text-align: center;
            margin-bottom: 20px;
        }
        .info-box {
            background-color: #E8F5E9;
            padding: 20px;
            border-radius: 12px;
            font-size: 16px;
            box-shadow: 0 2px 5px rgba(0,0,0,0.1);
            margin-bottom: 20px;
        }
        .css-1d391kg {
            background-color: #FFFFFF;
            border-right: 1px solid #E0E0E0;
        }
        .stButton>button {
            background-color: #2E7D32;
            color: white;
            border-radius: 12px;
            padding: 12px 24px;
            font-size: 16px;
            font-weight: 600;
            border: none;
            transition: all 0.3s ease;
        }
        .stButton>button:hover {
            background-color: #1A3C34;
            transform: scale(1.05);
        }
        .stTabs [data-baseweb="tab-list"] {
            gap: 20px;
        }
        .stTabs [data-baseweb="tab"] {
            background-color: #FFFFFF;
            border-radius: 8px;
            padding: 10px 20px;
            font-weight: 500;
            color: #1A3C34;
        }
        .stTabs [data-baseweb="tab"][aria-selected="true"] {
            background-color: #2E7D32;
            color: white;
        }
        .stImage > div > div > div > div > p {
            font-size: 14px;
            color: #4A4A4A;
            text-align: center;
        }
        .stMarkdown {
            font-size: 16px;
            color: #1A3C34;
        }
    </style>
""", unsafe_allow_html=True)

# Main header with emoji
st.markdown('<div class="main-header">🌱 Farmer\'s Friend 🌱</div>', unsafe_allow_html=True)
st.markdown('<div class="sub-header">Helping farmers worldwide keep crops healthy!</div>', unsafe_allow_html=True)

# Instruction section
st.markdown('<div class="info-box">📸 Upload a clear photo of your crop leaf to check for issues. We provide simple recommendations in English with English audio.</div>', unsafe_allow_html=True)

# Sidebar for settings
with st.sidebar:
    st.markdown("### 🌾 Crop Settings")
    crop_type = st.selectbox(
        "Select your crop type:",
        ["Wheat", "Maize", "Rice", "Corn", "Soybean", "Barley", "Cotton", "Millet", "Sorghum"],
        help="Choose the type of crop you're analyzing."
    )
    st.markdown("### ℹ️ About")
    st.write("This app helps farmers worldwide identify crop issues and get practical solutions. Upload a leaf photo to get started!")

# Tabs for organized layout
tab1, tab2 = st.tabs(["📤 Upload & Analyze", "📊 Results"])

with tab1:
    # Use columns for a clean layout
    col1, col2 = st.columns([1, 1])
    with col1:
        uploaded_image = st.file_uploader("Upload Photo:", type=["jpg", "jpeg", "png"], help="Upload a clear image of your crop leaf.")
        if uploaded_image:
            image = Image.open(uploaded_image)
            st.image(image, caption="Your Uploaded Photo", use_container_width=True)
        else:
            image = None

    with col2:
        st.markdown("### 📷 Tips for Best Results")
        st.write("- Use a close-up photo of the leaf.")
        st.write("- Ensure good lighting.")
        st.write("- Avoid blurry images.")

    # Analyze button
    if st.button("🔍 Check"):
        if image:
            with st.spinner("Analyzing your crop..."):
                result_text, audio_file = analyze_crop(image, crop_type)
                st.session_state['result_text'] = result_text
                st.session_state['audio_file'] = audio_file
                st.success("Analysis Complete! Check the Results tab.")
        else:
            st.warning("Please upload a photo first.")

with tab2:
    if 'result_text' in st.session_state:
        st.markdown("### 📊 Analysis Results")
        st.write(st.session_state['result_text'])
        if 'audio_file' in st.session_state and st.session_state['audio_file'] and os.path.exists(st.session_state['audio_file']):
            with open(st.session_state['audio_file'], 'rb') as f:
                audio_bytes = f.read()
            st.audio(audio_bytes, format='audio/mp3')
            # Clean up the temporary audio file after use
            try:
                os.remove(st.session_state['audio_file'])
            except:
                pass
        else:
            st.error("Audio not generated. Please try again.")
    else:
        st.info("No results yet. Please upload a photo and click 'Check' in the Upload tab.")

# Clear button at the bottom
if st.button("🔁 Clear"):
    # Clean up any existing audio files
    if 'audio_file' in st.session_state and st.session_state['audio_file'] and os.path.exists(st.session_state['audio_file']):
        try:
            os.remove(st.session_state['audio_file'])
        except:
            pass
    st.session_state.clear()
    st.rerun()

# Initial instruction audio
if not uploaded_image:
    instruction = "Upload a photo of your crop leaf to check for issues."
    instruction_audio = text_to_audio(instruction, "instruction.mp3")
    if instruction_audio and os.path.exists(instruction_audio):
        with open(instruction_audio, 'rb') as f:
            audio_bytes = f.read()
        st.audio(audio_bytes, format='audio/mp3')
        # Clean up the temporary audio file
        try:
            os.remove(instruction_audio)
        except:
            pass

# Footer
st.markdown("---")
st.markdown('<div style="text-align: center; color: #666;">Made with ❤️ for farmers worldwide | Contact support: [email protected]</div>', unsafe_allow_html=True)