Upload 2 files

app.py

@@ -0,0 +1,194 @@
+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
+from gtts import gTTS
+from googletrans import Translator
+import google.generativeai as genai  # Import Gemini API
+
+# Configure Google Gemini API
+GEMINI_API_KEY = os.getenv("GOOGLE_API_KEY")
+genai.configure(api_key=GEMINI_API_KEY)
+
+# Load YOLO model for crop disease detection
+yolo_model = YOLO("models/best.pt")
+
+# Initialize 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=(224, 224)):
+    """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 are: {', '.join(disease_list)}.
+        
+        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 += """
+        Provide a detailed diagnosis including:
+        1. Symptoms
+        2. Causes and risk factors
+        3. Impact on crops
+        4. Treatment options (short-term & long-term)
+        5. Prevention strategies
+        """
+
+        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)}"
+
+# Perform inference using YOLO
+def inference(image):
+    """Detect crop diseases in the given image."""
+    results = yolo_model(image, conf=0.4)
+    infer = np.zeros(image.shape, dtype=np.uint8)
+    detected_classes = []
+    class_names = {}
+
+    for r in results:
+        infer = r.plot()
+        class_names = r.names
+        detected_classes = r.boxes.cls.tolist()
+
+    return infer, detected_classes, class_names
+
+# Convert text to speech
+def text_to_speech(text, language="en"):
+    """Convert text to speech using gTTS."""
+    try:
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as temp_audio:
+            tts = gTTS(text=text, lang=language, slow=False)
+            tts.save(temp_audio.name)
+
+            with open(temp_audio.name, "rb") as audio_file:
+                audio_bytes = audio_file.read()
+
+            os.unlink(temp_audio.name)
+            return audio_bytes
+    except Exception as e:
+        st.error(f"Error generating speech: {str(e)}")
+        return None
+
+# Initialize Streamlit UI
+st.title("🌱 AI-Powered Crop Disease Detection & Diagnosis 🔬")
+
+# 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)
+    
+    # Text-to-Speech Settings
+    tts_enabled = st.checkbox("Enable Text-to-Speech", value=True)
+    language = st.selectbox("Speech Language", options=["en", "es", "fr", "de"], format_func=lambda x: {
+        "en": "English",
+        "es": "Spanish",
+        "fr": "French",
+        "de": "German"
+    }[x])
+
+    if st.button("Clear Conversation History"):
+        st.session_state.conversation_history = {}
+        st.success("Conversation history cleared!")
+
+# User context input
+st.subheader("📝 Provide Initial Context or Ask a Question")
+user_context = st.text_area("Enter any details, symptoms, or questions about the plant's condition.", placeholder="Example: My tomato plant leaves are turning yellow. Is it a disease or a nutrient deficiency?")
+
+# 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
+    processed_image, detected_classes, class_names = inference(img)
+
+    # Display processed image with detected diseases
+    st.image(processed_image, caption="🔍 Detected Diseases", use_column_width=True)
+
+    if detected_classes:
+        detected_disease_names = [class_names[cls] for cls in detected_classes]
+        st.write(f"✅ **Detected Diseases:** {', '.join(detected_disease_names)}")
+
+        # 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"])
+
+        # Convert diagnosis to speech if enabled
+        if tts_enabled:
+            if st.button("🔊 Listen to Diagnosis"):
+                with st.spinner("Generating audio... 🎵"):
+                    audio_bytes = text_to_speech(diagnosis, language)
+                    if audio_bytes:
+                        st.audio(audio_bytes, format="audio/mp3")
+
+    else:
+        st.write("❌ No crop disease detected.")
+
+# 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.
+6. Optionally, listen to the AI-generated diagnosis.
+""")

requirements.txt

@@ -0,0 +1,14 @@
+streamlit==1.41.1
+numpy==1.26.4
+opencv-python==4.9.0.80
+pillow==10.4.0
+ultralytics
+ollama==0.4.5
+gtts==2.5.1
+bcrypt==4.2.1
+googletrans-py==4.0.0
+huggingface-hub>=0.19
+hf-transfer>=0.1.4
+protobuf<4
+click<8.1
+google-generativeai