Create app.py

app.py

@@ -0,0 +1,71 @@
+"""
+app.py
+"""
+
+import base64
+
+import gradio as gr
+from groq import Groq
+
+# Initialize Groq client
+client = Groq()
+
+# Function to encode the image in base64
+def encode_image_to_base64(image):
+    # Convert PIL image to base64 string
+    buffered = BytesIO()
+    image.save(buffered, format="JPEG")
+    return base64.b64encode(buffered.getvalue()).decode('utf-8')
+
+# Function to process the uploaded image and extract receipt information
+def extract_receipt_info(image):
+    # Encode the image to base64
+    base64_image = encode_image_to_base64(image)
+`
+    # Send request to Groq API
+    chat_completion = client.chat.completions.create(
+        model="llama-3.2-11b-vision-preview",
+        messages=[
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "text",
+                        "text": "Your task is to extract key information from the provided receipt image.\n\nReply in table.\n\nThis is the schema:\n- item (str), description of the item\n- price (float), price of the item\n- quantity (int), quantity of the item\n- total (float), total cost for the item"
+                    },
+                    {
+                        "type": "image_url",
+                        "image_url": {
+                            "url": f"data:image/jpeg;base64,{base64_image}",
+                        },
+                    },
+                ],
+            },
+            {
+                "role": "assistant",
+                "content": "```markdown"
+            }
+        ],
+        temperature=0.1,
+        max_tokens=8192,
+        top_p=1,
+        stop="```",
+    )
+
+    # Return the response from the model
+    return chat_completion.choices[0].message.content
+
+# Create the Gradio app
+def gradio_app():
+    # Gradio interface
+    gr.Interface(
+        fn=extract_receipt_info,
+        inputs=gr.inputs.Image(type="pil", label="Upload Receipt Image"),
+        outputs="text",
+        title="Receipt Information Extractor",
+        description="Upload a receipt image and the model will extract the items, quantities, and prices from the receipt."
+    ).launch()
+
+# Start the Gradio app
+if __name__ == "__main__":
+    gradio_app()