Update app.py

app.py

@@ -1,76 +1,20 @@
-import os
-from io import BytesIO
 import gradio as gr
 import numpy as np
 import cv2
-from PIL import Image
-from dotenv import load_dotenv
-import google.generativeai as genai
-from model import classify_image  # Assuming this is a custom model for classification
+from model import classify_image
 
-# Load environment variables for Google Gemini API key
-load_dotenv()
-genai.configure(api_key=os.getenv("Google_Api_Key"))
-
-# Input prompt for Gemini model
-input_prompt = """
-    "You are an expert in computer vision and agriculture who can easily predict the disease of the plant. "
-    "Analyze the following image and provide 7 short outputs in a structured format: "
-    "1. Crop: , "
-    "2. Infected or Healthy: , "
-    "3. Type of disease (if any): , "
-    "4. Confidence out of 100% whether image is healthy or infected: , "
-    "5. Reason for the disease such as whether it is happening due to fungus, bacteria, insect bite, poor nutrition, etc.: , "
-    "6. Plant Growth Stage: , "
-    "7. Pest Life Stage: ."
-"""
-
-# Function to get a response from the Google Gemini API
-def get_gemini_response(image):
-    model = genai.GenerativeModel('gemini-1.5-pro')
-
-    # Convert PIL image to bytes
-    bytes_io = BytesIO()
-    image.save(bytes_io, format='PNG')
-    bytes_data = bytes_io.getvalue()
-
-    # Send image and prompt to Gemini API
-    response = model.generate_content([input_prompt, image])
-
-    # Extract the relevant response (mock response logic for now)
-    return response.text
-
-# Image classification using custom model (e.g., for detecting specific plant diseases)
-def classify_crop_image(img):
-    img = cv2.resize(img, (224, 224))
-    img = img / 255.0
+def main(img):
+    img = cv2.resize(img, (224,224))
+    img = img/255.0
     img = np.expand_dims(img, axis=0)
+    label, accuracy = classify_image(img)
+    print(label)
+    out = {label: accuracy}
+    return out
 
-    # Use the custom classification model to predict the disease
-    label, accuracy = classify_image(img)  # Assuming 'classify_image' returns label and accuracy
-    return {label: accuracy}
-
-# Function to handle the uploaded image, predict crop health, and provide a structured output
-def predict_crop_health(uploaded_image):
-    # Pass the image to the custom classifier for plant disease prediction
-    classification_result = classify_crop_image(np.array(uploaded_image))
-
-    # Pass the image to the Gemini API for detailed disease analysis
-    gemini_response = get_gemini_response(uploaded_image)
-
-    # Combine results from both models (custom and Gemini)
-    return f"Classification Result: {classification_result}\n\nGemini Response: {gemini_response}"
-
-# Define the Gradio interface: Inputs and Outputs
-inputs = gr.Image(type="pil", label="Upload Crop Image")
-outputs = gr.Textbox(label="Crop Disease Predictor", lines=10)
+demo = gr.Interface(fn=main,
+             inputs=gr.Image(),
+             outputs=gr.Label(num_top_classes=1), allow_flagging='never')
 
-# Launch the Gradio interface
-gr.Interface(
-    fn=predict_crop_health,
-    inputs=inputs,
-    outputs=outputs,
-    title="Crop Disease Prediction App",
-    description="Upload an image of a crop to predict its disease and get treatment suggestions.",
-    live=False
-).launch()
+if __name__ == "__main__":
+    demo.launch(share=True)"