Create app.py

app.py

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+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)