testing

app.py

@@ -1,120 +0,0 @@
-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 = """
-    .image_preview {
-        max-height: 700px; overflow-y: auto !important;
-    }
-
-    .big-font textarea {
-        font-size: 20px !important;
-    }
-
-    
-"""
-
-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(
-    fn=extract_text,
-    inputs=gr.Image(type="pil", elem_classes=["image_preview"]),  # Accept PIL images directly
-    outputs=gr.Textbox(lines=20,
-                       max_lines=10,
-                       label='Extracted Text',
-                       elem_classes=["big-font"]
-                       ),
-    title="Text Extraction",
-    description="Upload an image to extract text",
-    allow_flagging='never',
-    css=custom_css,
-)
-
-# Launch the app
-iface.launch()

gardio.py

@@ -0,0 +1,120 @@
+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 = """
+    .image_preview {
+        max-height: 700px; overflow-y: auto !important;
+    }
+
+    .big-font textarea {
+        font-size: 20px !important;
+    }
+
+    
+"""
+
+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(
+    fn=extract_text,
+    inputs=gr.Image(type="pil", elem_classes=["image_preview"]),  # Accept PIL images directly
+    outputs=gr.Textbox(lines=20,
+                       max_lines=10,
+                       label='Extracted Text',
+                       elem_classes=["big-font"]
+                       ),
+    title="Text Extraction",
+    description="Upload an image to extract text",
+    allow_flagging='never',
+    css=custom_css,
+)
+
+# Launch the app
+iface.launch()