Update app.py

app.py

@@ -1,4 +1,5 @@
 import os
+import openai
 import streamlit as st
 from PIL import Image
 from tensorflow.keras.models import load_model
@@ -10,6 +11,10 @@ UPLOAD_FOLDER = 'static/uploads'
 ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}
 TARGET_SIZE = (256, 256)
 
+# OpenAI API Key (make sure to keep this key secure and not expose it in public repositories)
+OPENAI_API_KEY = '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'
+openai.api_key = OPENAI_API_KEY
+
 # Ensure the upload folder exists
 os.makedirs(UPLOAD_FOLDER, exist_ok=True)
 
@@ -42,6 +47,14 @@ def predict_disease(image_path, model):
     pred = str(classes[index])
     return pred
 
+def get_disease_info(disease_name):
+    response = openai.Completion.create(
+        engine="text-davinci-003",
+        prompt=f"Explain the cause and solution for the following cotton plant disease: {disease_name}",
+        max_tokens=150
+    )
+    return response.choices[0].text.strip()
+
 # Streamlit app
 st.title("Cotton Disease Detection")
 st.write("Upload an image to detect the disease.")
@@ -66,6 +79,11 @@ if uploaded_file is not None:
         # Display the uploaded image
         st.image(image_path, caption='Uploaded Image.', use_column_width=True)
         st.write(f"Prediction: {prediction}")
+
+        # Get disease information from OpenAI GPT
+        disease_info = get_disease_info(prediction)
+        st.write("Disease Information:")
+        st.write(disease_info)
     else:
         st.write("Please upload an image file (png, jpg, jpeg).")
 else: