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