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app.py

@@ -9,10 +9,9 @@ import asyncio
 import nest_asyncio
 import os
 
-# Apply the nest_asyncio patch to allow nested event loops in Streamlit
+# Allow nested event loops in Streamlit
 nest_asyncio.apply()
 
-# Set page configuration with a custom title, icon, and wide layout
 st.set_page_config(
     page_title="Plant Disease Classifier 🌱",
     page_icon="🌱",
@@ -20,33 +19,17 @@ st.set_page_config(
     initial_sidebar_state="expanded",
 )
 
-# Custom CSS for styling
+# Custom CSS styling
 custom_css = """
 <style>
-body {
-    background-color: #f8f9fa;
-}
-h1, h2, h3, h4 {
-    color: #2c3e50;
-    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
-}
-.stButton>button {
-    background-color: #27ae60;
-    color: white;
-    border: none;
-    padding: 0.5em 1em;
-    border-radius: 5px;
-    font-size: 16px;
-}
-.sidebar .sidebar-content {
-    background-image: linear-gradient(#27ae60, #2ecc71);
-    color: white;
-}
+body { background-color: #f8f9fa; }
+h1, h2, h3, h4 { color: #2c3e50; font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; }
+.stButton>button { background-color: #27ae60; color: white; border: none; padding: 0.5em 1em; border-radius: 5px; font-size: 16px; }
+.sidebar .sidebar-content { background-image: linear-gradient(#27ae60, #2ecc71); color: white; }
 </style>
 """
 st.markdown(custom_css, unsafe_allow_html=True)
 
-# Dictionary mapping diseases to recommended pesticides (fallback recommendations)
 pesticide_recommendations = {
     'Bacterial Blight': 'Copper-based fungicides, Streptomycin',
     'Red Rot': 'Fungicides containing Mancozeb or Copper',
@@ -64,49 +47,26 @@ pesticide_recommendations = {
     'strip_rust': 'Fungicides containing Azoxystrobin or Propiconazole'
 }
 
-
 def recommend_pesticide(predicted_class):
     if predicted_class == 'Healthy':
         return 'No need for any pesticide, plant is healthy'
     return pesticide_recommendations.get(predicted_class, "No recommendation available")
 
-
+# Load an H5 Keras model directly.
 @st.cache_resource(show_spinner=False)
-def load_model_with_hub(model_path):
-    custom_objects = {"KerasLayer": hub.KerasLayer}
-    return tf.keras.models.load_model(model_path, custom_objects=custom_objects, compile=False)
+def load_h5_model(model_path):
+    return tf.keras.models.load_model(model_path, custom_objects={"KerasLayer": hub.KerasLayer}, compile=False)
 
-def load_hub_layer(model_path, input_shape):
-    # Create the Hub KerasLayer (using your model path)
-    hub_layer = hub.KerasLayer(model_path, 
-                               custom_objects={"KerasLayer": hub.KerasLayer},
-                               trainable=False,
-                               # You can pass input_shape if needed (for example, (224,224,3))
-                               input_shape=input_shape)
-    # Wrap it inside a Lambda layer so that Keras recognizes it as a native layer.
-    return tf.keras.layers.Lambda(lambda x: hub_layer(x), name="hub_lambda")
-
-
-# Load models (ensure your model paths are correct)
+# Define models dictionary.
 models = {
-    'sugarcane': tf.keras.Sequential([
-        load_hub_layer("models/sugercane_model.h5", (224, 224, 3))
-    ]),
-    'maize': tf.keras.Sequential([
-        load_hub_layer("models/maize_model.h5", (224, 224, 3))
-    ]),
-    'cotton': tf.keras.Sequential([
-        load_hub_layer("models/cotton_model.h5", (224, 224, 3))
-    ]),
-    'rice': tf.keras.Sequential([
-        load_hub_layer("models/rice.h5", (224, 224, 3))
-    ]),
-    'wheat': tf.keras.Sequential([
-        load_hub_layer("models/wheat_model.h5", (224, 224, 3))
-    ]),
+    'sugarcane': load_h5_model("models/sugercane_model.h5"),
+    'maize': load_h5_model("models/maize_model.h5"),
+    'cotton': load_h5_model("models/cotton_model.h5"),
+    'rice': load_h5_model("models/rice.h5"),
+    'wheat': load_h5_model("models/wheat_model.h5"),
 }
 
-# Class names for each model
+# Class names for each model (adjust these to match your models)
 class_names = {
     'sugarcane': ['Bacterial Blight', 'Healthy', 'Red Rot'],
     'maize': ['Blight', 'Common_Rust', 'Gray_Leaf_Spot,Healthy'],
@@ -115,9 +75,7 @@ class_names = {
     'wheat': ['Healthy', 'septoria', 'strip_rust'],
 }
 
-
 def preprocess_image(image_file):
-    """Preprocess the uploaded image: open, convert to RGB, resize, normalize, and add batch dimension."""
     try:
         image = Image.open(image_file).convert("RGB")
         image = image.resize((224, 224))
@@ -127,7 +85,6 @@ def preprocess_image(image_file):
         st.error("Error processing image. Please upload a valid image file.")
         return None
 
-
 def classify_image(model_name, image_file):
     input_image = preprocess_image(image_file)
     if input_image is None:
@@ -138,26 +95,20 @@ def classify_image(model_name, image_file):
     recommended_pesticide = recommend_pesticide(predicted_class)
     return predicted_class, recommended_pesticide
 
-
 def get_plant_info(disease, plant_type="Unknown"):
-    """
-    Retrieve detailed plant disease information from LM Studio using a fixed prompt.
-    """
     prompt = f"""
     Disease Name: {disease}
     Plant Type: {plant_type}
 
-    Explain this disease in a very simple and easy-to-understand way, as if you are talking to a farmer with no scientific background. Use simple words and avoid technical terms.
-
-    Include the following details:
-
-    - Symptoms: What signs will the farmer see on the plant? How will the leaves, stem, or fruit look?
-    - Causes: Why does this disease happen?
-    - Severity: How serious is this disease? Does it spread quickly? How much crop damage can it cause?
-    - How It Spreads: How does this disease grow? What will happen if the farmer does nothing?
-    - Treatment & Prevention: What pesticides or sprays should the farmer use and what steps can be taken to prevent the disease?
+    Explain this disease in a very simple way for a farmer. Include:
+    - Symptoms
+    - Causes
+    - Severity
+    - How It Spreads
+    - Treatment & Prevention
     """
     try:
+        LM_STUDIO_API_URL = os.getenv("LM_STUDIO_API_URL", "http://192.168.56.1:1234/v1/chat/completions")
         response = requests.post(LM_STUDIO_API_URL, json={"messages": [{"role": "user", "content": prompt}]})
         response.raise_for_status()
         data = response.json()
@@ -167,16 +118,12 @@ def get_plant_info(disease, plant_type="Unknown"):
         st.error("Error retrieving detailed plant info.")
         return {"detailed_info": ""}
 
-
 def get_web_pesticide_info(disease, plant_type="Unknown"):
-    """
-    Query Google Custom Search for updated pesticide recommendations.
-    """
     query = f"site:agrowon.esakal.com {disease} in {plant_type}"
     url = "https://www.googleapis.com/customsearch/v1"
     params = {
-        "key": GOOGLE_API_KEY,
-        "cx": GOOGLE_CX,
+        "key": os.getenv("GOOGLE_API_KEY"),
+        "cx": os.getenv("GOOGLE_CX"),
         "q": query,
         "num": 3
     }
@@ -186,23 +133,19 @@ def get_web_pesticide_info(disease, plant_type="Unknown"):
         data = response.json()
         if "items" in data and len(data["items"]) > 0:
             item = data["items"][0]
-            title = item.get("title", "No title available")
-            link = item.get("link", "#")
-            snippet = item.get("snippet", "No snippet available")
-            return {"title": title, "link": link, "snippet": snippet, "summary": snippet}
+            return {"title": item.get("title", "No title available"),
+                    "link": item.get("link", "#"),
+                    "snippet": item.get("snippet", "No snippet available"),
+                    "summary": item.get("snippet", "No snippet available")}
     except Exception as e:
         st.error("Error retrieving web pesticide info.")
     return None
 
-
 def get_more_web_info(query):
-    """
-    Query Google Custom Search for more articles or information.
-    """
     url = "https://www.googleapis.com/customsearch/v1"
     params = {
-        "key": GOOGLE_API_KEY,
-        "cx": GOOGLE_CX,
+        "key": os.getenv("GOOGLE_API_KEY"),
+        "cx": os.getenv("GOOGLE_CX"),
         "q": query,
         "num": 3
     }
@@ -213,53 +156,31 @@ def get_more_web_info(query):
         results = []
         if "items" in data:
             for item in data["items"]:
-                title = item.get("title", "No title available")
-                link = item.get("link", "#")
-                snippet = item.get("snippet", "No snippet available")
-                results.append({"title": title, "link": link, "snippet": snippet})
+                results.append({"title": item.get("title", "No title available"),
+                                "link": item.get("link", "#"),
+                                "snippet": item.get("snippet", "No snippet available")})
         return results
     except Exception as e:
         st.error("Error retrieving additional articles.")
     return []
 
-
 def get_commercial_product_info(recommendation):
-    """
-    Query Google Custom Search for commercial product details from IndiaMART and Krishisevakendra.
-    """
     indiamart_query = f"site:indiamart.com pesticide '{recommendation}'"
     krishi_query = f"site:krishisevakendra.in/products pesticide '{recommendation}'"
     indiamart_results = get_more_web_info(indiamart_query)
     krishi_results = get_more_web_info(krishi_query)
     return indiamart_results + krishi_results
 
-
-# LM Studio API endpoint for detailed info (do not change key prompt values)
-LM_STUDIO_API_URL = os.getenv("LM_STUDIO_API_URL", "http://192.168.56.1:1234/v1/chat/completions")
-
-# Google Custom Search API key and CX
-GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
-GOOGLE_CX = os.getenv("GOOGLE_CX")
-
-# Initialize session state for language if not already done
+translator = Translator()
 if "language" not in st.session_state:
     st.session_state.language = "English"
 
-# Initialize Google Translator
-translator = Translator()
-
-
-# --- Async translation functions ---
 async def async_translate_text(text):
     translated = await translator.translate(text, src='en', dest='mr')
     return translated.text
 
-
 def translate_text(text):
-    """
-    Translate text to Marathi if selected, otherwise return original text.
-    """
-    if st.session_state.language == "Marathi":
+    if st.session_state.get("language", "English") == "Marathi":
         try:
             return asyncio.get_event_loop().run_until_complete(async_translate_text(text))
         except Exception as e:
@@ -267,9 +188,7 @@ def translate_text(text):
             return text
     return text
 
-
 def main():
-    # Sidebar with settings and file uploader
     st.sidebar.title("Settings")
     st.sidebar.info("Choose language and plant type, then upload an image to classify the disease.")
     language_option = st.sidebar.radio("Language", options=["English", "Marathi"], index=0)
@@ -277,37 +196,25 @@ def main():
     plant_type = st.sidebar.selectbox("Select Plant Type", options=['sugarcane', 'maize', 'cotton', 'rice', 'wheat'])
     uploaded_file = st.sidebar.file_uploader("Upload a plant image...", type=["jpg", "jpeg", "png"])
 
-    # Header with a banner image and introductory text
     col1, col2 = st.columns([1, 2])
     with col1:
-        st.image("https://via.placeholder.com/150x150.png?text=Plant", caption=translate_text("Plant Health"),
-                 use_container_width=True)
+        st.image("https://via.placeholder.com/150.png?text=Plant", caption=translate_text("Plant Health"), use_container_width=True)
     with col2:
-        st.title(translate_text("Krushi Mitra "))
-        st.write(translate_text(
-            "Plant Disease Classification and Pesticide Recommendation.\n\n"
-            "Upload an image of your plant, select the plant type from the sidebar, and click on Classify to get the diagnosis and recommendations."))
+        st.title(translate_text("Krushi Mitra"))
+        st.write(translate_text("Plant Disease Classification and Pesticide Recommendation.\n\nUpload an image, select plant type, and click on Classify."))
 
     if uploaded_file is not None:
-        # Display the uploaded image in an appealing container
         st.markdown("---")
         st.subheader(translate_text("Uploaded Image"))
         st.image(uploaded_file, use_container_width=True)
-
         if st.button(translate_text("Classify")):
             with st.spinner(translate_text("Classifying...")):
                 predicted_class, pesticide = classify_image(plant_type, uploaded_file)
             if predicted_class:
                 st.success(translate_text("Classification Complete!"))
-                st.markdown(
-                    f"### {translate_text('Predicted Class')} ({plant_type.capitalize()}): {translate_text(predicted_class)}")
+                st.markdown(f"### {translate_text('Predicted Class')} ({plant_type.capitalize()}): {translate_text(predicted_class)}")
                 st.markdown(f"### {translate_text('Recommended Pesticide')}: {translate_text(pesticide)}")
-
-                # Display results in tabs for a cleaner layout
-                tabs = st.tabs([translate_text("Detailed Info"), translate_text("Commercial Products"),
-                                translate_text("More Articles")])
-
-                # Detailed Info Tab
+                tabs = st.tabs([translate_text("Detailed Info"), translate_text("Commercial Products"), translate_text("More Articles")])
                 with tabs[0]:
                     with st.spinner(translate_text("Retrieving detailed plant information...")):
                         info = get_plant_info(predicted_class, plant_type)
@@ -316,43 +223,34 @@ def main():
                         st.markdown(translate_text(info.get("detailed_info")))
                     else:
                         st.info(translate_text("Detailed information is not available at the moment."))
-
-                    # Inline web pesticide recommendations
                     web_recommendation = get_web_pesticide_info(predicted_class, plant_type)
                     if web_recommendation:
                         st.markdown(translate_text("#### Additional Pesticide Recommendations"))
-                        st.markdown(f"{translate_text('Title')}:** {translate_text(web_recommendation['title'])}")
-                        st.markdown(f"{translate_text('Summary')}:** {translate_text(web_recommendation['summary'])}")
+                        st.markdown(f"**{translate_text('Title')}:** {translate_text(web_recommendation['title'])}")
+                        st.markdown(f"**{translate_text('Summary')}:** {translate_text(web_recommendation['summary'])}")
                         if web_recommendation['link']:
                             st.markdown(f"[{translate_text('Read More')}]({web_recommendation['link']})")
                     else:
                         st.info(translate_text("No additional pesticide recommendations available."))
-
-
-                # Commercial Products Tab
                 with tabs[1]:
                     with st.spinner(translate_text("Retrieving commercial product details...")):
                         commercial_products = get_commercial_product_info(pesticide)
                     if commercial_products:
                         for item in commercial_products:
-                            st.markdown(f"{translate_text('Title')}:** {translate_text(item['title'])}")
-                            st.markdown(f"{translate_text('Snippet')}:** {translate_text(item['snippet'])}")
+                            st.markdown(f"**{translate_text('Title')}:** {translate_text(item['title'])}")
+                            st.markdown(f"**{translate_text('Snippet')}:** {translate_text(item['snippet'])}")
                             if item['link']:
                                 st.markdown(f"[{translate_text('Read More')}]({item['link']})")
                             st.markdown("---")
                     else:
                         st.info(translate_text("No commercial product details available."))
-
-
-
-                # More Articles Tab
                 with tabs[2]:
                     with st.spinner(translate_text("Retrieving additional articles...")):
                         more_info = get_more_web_info(f"{predicted_class} in {plant_type}")
                     if more_info:
                         for item in more_info:
-                            st.markdown(f"{translate_text('Title')}:** {translate_text(item['title'])}")
-                            st.markdown(f"{translate_text('Snippet')}:** {translate_text(item['snippet'])}")
+                            st.markdown(f"**{translate_text('Title')}:** {translate_text(item['title'])}")
+                            st.markdown(f"**{translate_text('Snippet')}:** {translate_text(item['snippet'])}")
                             if item['link']:
                                 st.markdown(f"[{translate_text('Read More')}]({item['link']})")
                             st.markdown("---")
@@ -363,6 +261,5 @@ def main():
     else:
         st.info(translate_text("Please upload an image from the sidebar to get started."))
 
-
-if __name__ == "_main_":
-    main()
+if __name__ == "__main__":
+    main()

test.py

@@ -0,0 +1,8 @@
+import tensorflow as tf
+
+# Load your H5 model (using custom_objects if necessary)
+custom_objects = {"KerasLayer": tf.keras.layers.Layer}  # adjust if needed
+model = tf.keras.models.load_model("models/sugercane_model.h5", custom_objects=custom_objects, compile=False)
+
+# Save in the SavedModel format
+tf.keras.models.save_model(model, "models/sugercane_model_saved", save_format="tf")