Update app.py

app.py

@@ -1,20 +1,60 @@
+import os
+from io import BytesIO
 import gradio as gr
-import numpy as np
-import cv2
-from model import classify_image
-
-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
-
-demo = gr.Interface(fn=main,
-             inputs=gr.Image(),
-             outputs=gr.Label(num_top_classes=1), allow_flagging='never')
-
-if __name__ == "__main__":
-    demo.launch(share=True)
+import google.generativeai as genai
+from PIL import Image
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+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. How confident 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 Vision 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()
+    response=model.generate_content([input_prompt,image])
+    # Placeholder logic for now: replace with actual API usage
+    # You would pass the image bytes data to the API
+    # Currently this is a mock response
+    crop_name = "Wheat"  # Example: Replace this with the actual API result
+    disease_type = "Wheat Septoria"  # Example: Replace this with the actual API result
+
+    # Return disease info
+    #return get_disease_info(crop_name, disease_type)
+    return response.text
+
+# Function to handle the uploaded image and predict crop health
+def predict_crop_health(uploaded_image):
+    # Pass the image to the Gemini API to get prediction
+    return get_gemini_response(uploaded_image)
+
+# Define the Gradio interface: Inputs and Outputs
+inputs = gr.Image(type="pil", label="Upload Crop Image")
+outputs = gr.Markdown(label="Prediction Results")
+
+# Launch the Gradio interface
+gr.Interface(
+    fn=predict_crop_health,
+    inputs=inputs,
+    outputs=gr.Textbox(label="Crop Disease Predictor"),
+    title="Crop Disease Prediction App",
+    description="Upload an image of a crop to predict its disease and get treatment suggestions.",
+    live=False
+).launch()