testing

app.py

@@ -1,6 +1,68 @@
 import gradio as gr
 from PIL import Image
 import pytesseract
+import json 
+
+import google.generativeai as genai 
+google_api = 'AIzaSyAMlYqwvuQgekl8nlqc56XTqJVBufszrBU'
+genai.configure(api_key=google_api)
+from pathlib import Path
+from IPython.display import Markdown
+
+
+
+
+from PIL import Image
+import io 
+
+
+
+
+# Model Configuration
+MODEL_CONFIG = {
+  "temperature": 0.2,
+  "top_p": 1,
+  "top_k": 32,
+  "max_output_tokens": 4096,
+}
+
+## Safety Settings of Model
+safety_settings = [
+  {
+    "category": "HARM_CATEGORY_HARASSMENT",
+    "threshold": "BLOCK_MEDIUM_AND_ABOVE"
+  },
+  {
+    "category": "HARM_CATEGORY_HATE_SPEECH",
+    "threshold": "BLOCK_MEDIUM_AND_ABOVE"
+  },
+  {
+    "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
+    "threshold": "BLOCK_MEDIUM_AND_ABOVE"
+  },
+  {
+    "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
+    "threshold": "BLOCK_MEDIUM_AND_ABOVE"
+  }
+]
+
+model = genai.GenerativeModel(model_name='gemini-2.5-flash',
+                              generation_config=MODEL_CONFIG,
+                              safety_settings=safety_settings)
+
+
+def gemini_output(image_path,
+                  system_prompt,
+                  user_prompt):
+    
+    
+    input_prompt = [system_prompt, image_path, user_prompt]
+    response = model.generate_content(input_prompt)
+
+    return response.text
+
+
+
 
 custom_css = """
     .big-font textarea {
@@ -8,10 +70,30 @@ custom_css = """
     }
 """
 
-def extract_text(image):
-    # Convert the image to text using pytesseract
-    text = pytesseract.image_to_string(image)
-    return text
+def extract_text(image_path):
+   
+    system_prompt = """
+               You are a specialist in comprehending receipts.
+               Input images in the form of receipts will be provided to you,
+               and your task is to respond to questions based on the content of the input image.
+               """
+    
+    user_prompt = "Convert Invoice data into json format with appropriate json tags as required for the data in image "
+    output = gemini_output(image_path, system_prompt, user_prompt)
+
+    
+
+    output = output.replace("```json", "")
+    output = output.replace("```", "")
+    
+
+    print(f">>>>>>> {output}")
+    
+    
+
+
+    return output
+
 
 # Create the Gradio interface
 iface = gr.Interface(

requirements.txt

@@ -1,3 +1,4 @@
 pytesseract
 pillow
-gradio
+gradio
+google-generativeai