Create app.py

app.py

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+import streamlit as st
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from PIL import Image
+import torch
+import googletrans
+from googletrans import Translator
+import os
+from groq import Groq
+
+# Load Model
+MODEL_NAME = "linkanjarad/mobilenet_v2_1.0_224-plant-disease-identification"
+processor = AutoImageProcessor.from_pretrained(MODEL_NAME)
+model = AutoModelForImageClassification.from_pretrained(MODEL_NAME)
+
+# Groq API Key (Set in Hugging Face Secrets)
+GROQ_API_KEY = os.getenv("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")
+client = Groq(api_key=GROQ_API_KEY)
+
+# Disease Descriptions
+disease_info = {
+    "Bacterial Spot": {"cause": "Bacteria (Xanthomonas spp.)", "remedy": "Use copper-based fungicides."},
+    "Leaf Mold": {"cause": "Fungus (Cladosporium fulvum)", "remedy": "Use resistant plant varieties."},
+    "Healthy": {"cause": "No disease detected", "remedy": "Your plant is healthy!"}
+}
+
+# Translator
+translator = Translator()
+
+# Streamlit UI
+st.set_page_config(page_title="Plant Disease Detection", page_icon="🌿", layout="wide")
+
+st.title("🌿 Plant Disease Detection App")
+st.write("Upload a leaf image to detect diseases and get solutions.")
+
+# Image Upload
+uploaded_file = st.file_uploader("📷 Upload a leaf image...", type=["jpg", "png", "jpeg"])
+
+if uploaded_file:
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    # Predict Disease
+    inputs = processor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        logits = model(**inputs).logits
+
+    predicted_class_idx = logits.argmax(-1).item()
+    predicted_label = model.config.id2label[predicted_class_idx]
+    confidence = torch.nn.functional.softmax(logits, dim=-1)[0][predicted_class_idx].item() * 100
+
+    st.subheader(f"🔍 **Detected Disease:** {predicted_label} ({confidence:.2f}%)")
+
+    # Get Disease Info
+    if predicted_label in disease_info:
+        cause = disease_info[predicted_label]["cause"]
+        remedy = disease_info[predicted_label]["remedy"]
+    else:
+        cause = "Unknown cause."
+        remedy = "Consult an expert."
+
+    # Select Language
+    language = st.selectbox("🌍 Select Language", list(googletrans.LANGUAGES.values()), index=21)  # Default: English
+
+    lang_code = list(googletrans.LANGUAGES.keys())[list(googletrans.LANGUAGES.values()).index(language)]
+
+    # Translate Disease Info
+    cause_translated = translator.translate(cause, dest=lang_code).text
+    remedy_translated = translator.translate(remedy, dest=lang_code).text
+
+    st.info(f"🦠 **Cause:** {cause_translated}")
+    st.success(f"💊 **Remedy:** {remedy_translated}")
+
+    # Chatbot
+    st.subheader("💬 Chat with AI about Plant Diseases")
+    user_query = st.text_input("Type your question about the disease:")
+
+    if user_query:
+        response = client.chat.completions.create(
+            messages=[{"role": "user", "content": user_query}],
+            model="llama-3.3-70b-versatile"
+        )
+        chatbot_response = response.choices[0].message.content
+        st.write("🤖 **Chatbot Response:**", chatbot_response)