yes

app.py

@@ -0,0 +1,30 @@
+import streamlit as st
+from streamlit_option_menu import option_menu
+
+# Inclure le logo
+st.image('logo', width=700)  # Adjust the width as needed
+
+# Description de l'application
+st.markdown("""
+    <div style="color: #000000; font-size: 20px; font-weight: bold; text-align: center; margin-bottom: 20px;">
+This application helps farmers detect plant leaf diseases and generate reports with recommended solutions. Additionally, it offers a weather dashboard to help you plan your agricultural activities.
+    </div>
+""", unsafe_allow_html=True)
+
+
+
+
+selected = option_menu(
+        menu_title=None,
+        options=["home","Today's weather", "Leaf disease detection"],
+        icons=['weather', 'image'],
+        menu_icon="cast", default_index=0, orientation="horizontal",
+        
+    )
+if selected == "Today's weather":
+    st.switch_page('pages/Weather.py')
+elif selected == "Leaf disease detection":
+     st.switch_page('pages/LeafDiseaseDetection.py')
+
+
+

pages/LeafDiseaseDetection .py

@@ -0,0 +1,61 @@
+import streamlit as st
+from ultralytics import YOLO
+from PIL import Image
+import numpy as np
+from groq import Groq
+
+client = Groq(
+    api_key="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"
+)
+
+def process_diseases(diseases):
+    unique_elements = sorted(set(diseases))
+    diseases_str = ", ".join(unique_elements)
+    user_input = f"How can {diseases_str}  leaf diseases be treated?"
+    return user_input
+
+
+# Function to detect diseases in an image
+def detect_image(image):
+    detected_classes = []
+    img_array = np.array(image)
+
+    model = YOLO("yolo.pt")
+    results = model(img_array)
+
+    for result in results:
+        img_with_boxes = result.plot()
+        for box in result.boxes:
+            class_id = int(box.cls)
+            class_name = model.names[class_id]
+            detected_classes.append(class_name)
+
+    return img_with_boxes, list(dict.fromkeys(detected_classes))
+
+def get_chat_completion(prompt):
+    
+    chat_completion = client.chat.completions.create(
+    messages=[{ "role": "user", "content": prompt, } ],
+    model="llama3-8b-8192",)
+    response=chat_completion.choices[0].message.content
+    return response
+
+st.title("Disease Detection and Solution Finder")
+
+uploaded_file = st.file_uploader("Upload image", type=["png", "jpg", "jpeg","webp"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    detected_image, diseases = detect_image(image)
+    detected_pil_image = Image.fromarray(detected_image)
+    st.image(detected_pil_image, caption='Detected Image', width=500)
+    user_input = process_diseases(diseases)
+
+    if st.button("Get Solution"):
+        if user_input:
+            st.write(user_input)
+            with st.spinner("Generating response..."):
+                response = get_chat_completion(user_input)
+            st.write(response)  # Change st.write_stream to st.write
+        else:
+            st.write("No diseases detected to generate a solution.")

pages/Weather.py

@@ -0,0 +1,11 @@
+import streamlit as st
+# Exemple d'utilisation de st.components.v1.iframe
+iframe_src ="https://app.powerbi.com/view?r=eyJrIjoiNzg4M2E3YzQtYmUyZS00MWE1LTlhYTMtYWZhYjAxMzcyNGM0IiwidCI6ImRiZDY2NjRkLTRlYjktNDZlYi05OWQ4LTVjNDNiYTE1M2M2MSIsImMiOjl9" 
+
+st.components.v1.iframe(
+    src=iframe_src,
+    width=1000,
+    height=1200,
+    scrolling=True  # Permet le défilement si nécessaire
+)
+

pages/yolo.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b2142cdc3e0211e115813b924500e4af755889b153e5df40602ca5a8cef2032a
+size 6250275