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.env

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+GOOGLE_API_KEY="AIzaSyDs9p7qZMOl8XG_th5QPZ7ILgMlhzSxSBw"

app.py

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+##Importing All the modules
+import streamlit as st
+import os
+from PIL import Image
+import google.generativeai as genai
+from dotenv import load_dotenv
+
+
+# Load all environment Variables
+load_dotenv()
+
+##Configuring the api key
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+## Function to load Gemini Vison Pro Vision Model and Get response
+
+def get_gemini_response(input,image,prompt):
+
+    ##Loading the desired Model
+    model= genai.GenerativeModel("gemini-pro-vision")
+    response=model.generate_content([input,image[0],prompt])
+    return response.text
+
+## Function to extract data from Image Uploaded
+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")
+    
+#       Initializing our Streamlit Prompt
+
+st.set_page_config(page_title="Medical Prescription Extractor")
+
+st.header("Prescription Analyzer")
+input=st.text_input("Optional Hint for the Model (e.g., Extract dosage): ",key="input")
+uploaded_file = st.file_uploader("Choose a Prescription Image...", type=["jpg", "jpeg", "png"])
+image=""   
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Prescription.", use_column_width=True)
+
+
+submit=st.button("Analyze Prescription")
+
+##  DEFINING A SYSTEM PROMPT
+
+input_prompt = "You are a licensed medical professional trained to interpret prescriptions. You will receive prescription images as input and you will have to extract key information like dosage, strength, frequency, and medication name."
+
+if submit:
+    image_data = input_image_setup(uploaded_file)
+    response = get_gemini_response(input,image_data,input_prompt)
+
+    st.subheader("Analysis by your model Gemini Pro Vision: ")
+    st.write(response)
+
+    st.balloons()