Update app.py

app.py

@@ -1,37 +1,67 @@
-import gradio as gr
-from google.generativeai import GenerativeModel  # Adjust this based on available imports
-
-# Initialize the model (check the correct model name from the documentation)
-model = GenerativeModel(model_name="gemini-1.5-flash")  # Adjust based on actual available model names
-
-def get_gemini_response(image_data):
-    input_prompt = "Analyze the uploaded plant image."
-
-    # 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()
+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)