app.py

import streamlit as st
import numpy as np
import cv2
from PIL import Image
from io import BytesIO
#from ultralytics import YOLO
import os
import tempfile
import base64
import requests
from datetime import datetime
import google.generativeai as genai  # Import Gemini API
from tensorflow.keras.models import load_model
from vit import vit_classifier

# Load the model
model = load_model('vit_updated.weights.h5')  # Replace with your model file path
# Configuring Google Gemini API
GEMINI_API_KEY = os.getenv("GOOGLE_API_KEY")
genai.configure(api_key=GEMINI_API_KEY)

# Loading YOLO model for crop disease detection
#yolo_model = YOLO("models/best.pt")

# Initializing conversation history if not set
if "conversation_history" not in st.session_state:
    st.session_state.conversation_history = {}

# Function to preprocess images
def preprocess_image(image, target_size=(256, 256)):
    """Resize image for AI models."""
    image = Image.fromarray(image)
    image = image.resize(target_size)
    return image

# Generate response from Gemini AI with history
def generate_gemini_response(disease_list, user_context="", conversation_history=None):
    """Generate a structured diagnosis using Gemini API, considering conversation history."""
    try:
        model = genai.GenerativeModel("gemini-1.5-pro")

        # Start with detected diseases
        prompt = f"""
        You are an expert plant pathologist. The detected crop diseases is: {predicted_labels}.
        
        User's context or question: {user_context if user_context else "Provide a general analysis"}
        """

        # Add past conversation history for better continuity
        if conversation_history:
            history_text = "\n\nPrevious conversation:\n"
            for entry in conversation_history:
                history_text += f"- User: {entry['question']}\n- AI: {entry['response']}\n"
            prompt += history_text

        # Ask Gemini for a structured diagnosis
        prompt += """
        For each detected disease, provide a structured analysis following this format:
        1. Disease Name: [Name]
       - Pathogen: [Causative organism]
       - Severity Level: [Based on visual symptoms]
       - Key Symptoms:
         * [Symptom 1]
         * [Symptom 2]
       - Economic Impact:
         * [Brief description of potential crop losses]
       - Treatment Options:
         * Immediate actions: [Short-term solutions]
         * Long-term management: [Preventive measures]
       - Environmental Conditions:
         * Favorable conditions for disease development
         * Risk factors
        2. Recommendations:
       - Immediate Steps:
         * [Action items for immediate control]
       - Prevention Strategy:
         * [Long-term prevention measures]
       - Monitoring Protocol:
         * [What to watch for]"""


        response = model.generate_content(prompt)
        return response.text if response else "No response from Gemini."
    except Exception as e:
        return f"Error connecting to Gemini API: {str(e)}"

# Performing inference using YOLO
def inference(image):
    """Detect crop diseases in the given image with confidence filtering."""
    predictions = vit_classifier.predict(image)
    predicted_labels = np.argmax(predictions, axis=1)

    return predicted_labels




# Initialize Streamlit UI
st.title("AI-Powered Crop Disease Detection & Diagnosis System")

# Sidebar settings
with st.sidebar:
    st.header("Settings")

    # Fake model selection (Still uses Gemini)
    selected_model = st.selectbox("Choose Model", ["Gemini", "GPT-4", "Claude", "Llama 3", "Mistral"], help="This app always uses Gemini.")

    confidence_threshold = st.slider("Detection Confidence Threshold", 0.0, 1.0, 0.4)
    
  

    if st.button("Clear Conversation History"):
        st.session_state.conversation_history = {}
        st.success("Conversation history cleared!")


# User context input with example prompts
st.subheader("Provide Initial Context or Ask a Question")

# Generalized example prompts for easier input
example_prompts = {
    "Select an example...": "",
    "General Plant Health Issue": "My plant leaves are wilting and turning yellow. Is this a disease or a nutrient deficiency?",
    "Leaf Spots and Discoloration": "I see dark spots on my crop leaves. Could this be a fungal or bacterial infection?",
    "Leaves Drying or Curling": "The leaves on my plants are curling and drying up. What could be causing this?",
    "Pest or Disease?": "I noticed tiny insects on my plants along with some leaf damage. Could this be a pest problem or a disease?",
    "Overwatering or Root Rot?": "My plant leaves are turning brown and mushy. Is this due to overwatering or a root infection?",
    "Poor Crop Growth": "My crops are growing very slowly and seem weak. Could this be due to soil problems or disease?",
    "Weather and Disease Connection": "It has been raining a lot, and now my plants have mold. Could the weather be causing a fungal disease?",
    "Regional Disease Concern": "I'm in a humid area and my crops often get infected. What are common diseases for this climate?",
}

# Dropdown menu for selecting an example
selected_example = st.selectbox("Choose an example to auto-fill:", list(example_prompts.keys()))

# Auto-fill the text area when an example is selected
user_context = st.text_area(
    "Enter details, symptoms, or a question about your plant condition.",
    value=example_prompts[selected_example] if selected_example != "Select an example..." else "",
    placeholder="Example: My plant leaves are turning yellow and wilting. Is this a disease or a nutrient issue?"
)

# Upload an image
uploaded_file = st.file_uploader("📤 Upload a plant image", type=["jpg", "jpeg", "png"])

if uploaded_file:
    file_id = uploaded_file.name

    # Initialize conversation history for this image if not set
    if file_id not in st.session_state.conversation_history:
        st.session_state.conversation_history[file_id] = []

    # Convert file to image
    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
    img = cv2.imdecode(file_bytes, 1)

    # Perform inference
    predicted_labels = inference(img)

    # Display processed image with detected diseases
    st.image(img, caption="🔍 Detected Diseases", use_column_width=True)

  
    st.write(f"✅ **High Confidence Diseases Detected:** {predicted_labels}")
    


        # AI-generated diagnosis from Gemini
    st.subheader("📋 AI Diagnosis")
    with st.spinner("Generating diagnosis... 🔄"):
         diagnosis = generate_gemini_response(detected_disease_names, user_context, st.session_state.conversation_history[file_id])

        # Save response to history
    st.session_state.conversation_history[file_id].append({"question": user_context, "response": diagnosis})

        # Display the diagnosis
    st.write(diagnosis)

        # Show past conversation history
    if st.session_state.conversation_history[file_id]:
        st.subheader("🗂️ Conversation History")
        for i, entry in enumerate(st.session_state.conversation_history[file_id]):
            with st.expander(f"Q{i+1}: {entry['question'][:50]}..."):
                st.write("**User:**", entry["question"])
                st.write("**AI:**", entry["response"]) 
       

# Instructions for users
st.markdown("""
---
### How to Use:
1. Upload an image of a plant leaf with suspected disease.
2. Provide context (optional) about symptoms or concerns.
3. The system detects the disease using AI.
4. Gemini generates a diagnosis with symptoms and treatments.
5. Ask follow-up questions, and the AI will remember previous responses.
""")