Update app.py

app.py

@@ -1,68 +1,37 @@
-### Health Management APP
-from dotenv import load_dotenv
-
-load_dotenv() ## load all the environment variables
-
-import streamlit as st
-import os
-import google.generativeai as genai
-from PIL import Image
-
-genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
-
-## Function to load Google Gemini Pro Vision API And get response
-
-def get_gemini_repsonse(input,image,prompt):
-    model=genai.GenerativeModel('gemini-1.5-flash')
-    response=model.generate_content([input,image[0],prompt])
-    return response.text
-
-def input_image_setup(uploaded_file):
-    # Check if a file has been uploaded
-    if uploaded_file is not None:
-        # Read the file into bytes
-        bytes_data = uploaded_file.getvalue()
-
-        image_parts = [
-            {
-                "mime_type": uploaded_file.type,  # Get the mime type of the uploaded file
-                "data": bytes_data
-            }
-        ]
-        return image_parts
-    else:
-        raise FileNotFoundError("No file uploaded")
-    
-##initialize our streamlit app
-
-st.set_page_config(page_title="Crop Disease Detection App")
-
-st.header("Gemini Crop Disease App")
-input=st.text_input("Input Prompt: ",key="input")
-uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-image=""   
-if uploaded_file is not None:
-    image = Image.open(uploaded_file)
-    st.image(image, caption="Uploaded Image.", use_column_width=True)
-
-
-submit=st.button("Predict Crop/Plant Health")
-
-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 6 outputs in a structured table format: "
-        "1. Crop in the image, "
-        "2. Whether it is 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. Precautions for it."
-"""
-
-## If submit button is clicked
-
-if submit:
-    image_data=input_image_setup(uploaded_file)
-    response=get_gemini_repsonse(input_prompt,image_data,input)
-    st.subheader("The Response is")
-    st.write(response)
+import gradio as gr
+from google.generativeai import generative_language_v1beta
+
+# Initialize the model
+model = generative_language_v1beta.GenerativeModel(model_name="gemini-1.5-flash")
+
+def get_gemini_response(image_data):
+    input_prompt = "Analyze the uploaded plant image."
+
+    # Use the model to generate a response based on the uploaded image
+    response = model.generate_content([input_prompt, image_data])
+
+    # Process the response to extract relevant information
+    output = {
+        "Crop in the image": response.get("crop", "Unknown"),  # Adjust based on actual response structure
+        "Whether it is infected or healthy": response.get("health_status", "Unknown"),  # Actual response
+        "Type of disease (if any)": response.get("disease_type", "None"),  # Actual response
+        "Confidence percentage": response.get("confidence", "N/A"),  # Actual response
+        "Reason for the disease": response.get("reason", "Not specified"),  # Actual response
+        "Precautions for it": response.get("precautions", "None provided")  # Actual response
+    }
+
+    # Format the output
+    output_string = "The Response is:\n" + "\n".join([f"{key}: {value}" for key, value in output.items()])
+    return output_string
+
+# Set up the Gradio interface
+iface = gr.Interface(
+    fn=get_gemini_response,
+    inputs=gr.Image(type="pil"),  # Accept image uploads
+    outputs="text",  # Output text response
+    title="Plant Disease Diagnosis",
+    description="Upload a plant image to analyze its health status and identify any diseases."
+)
+
+# Launch the Gradio application
+iface.launch()