Gradio + fastapi

app.py

@@ -1,21 +1,20 @@
 from fastapi import FastAPI
 from pydantic import BaseModel
 import google.generativeai as genai
+import gradio as gr
 
-# Configure the API key for Google Gemini
-genai.configure(api_key="AIzaSyCZKxLPz1lcO5QrATDYbgJmOZHOk4aIKuA")
+# Configure the Google Gemini API key
+genai.configure(api_key="YOUR_API_KEY")
 
-# Create a FastAPI instance
+# FastAPI app
 app = FastAPI()
 
-# Define the OCRText model to accept input
+# Pydantic model for OCR text input
 class OCRText(BaseModel):
     ocr_text: str
 
-# Define the /extract endpoint
 @app.post("/extract")
 async def extract_data(request: OCRText):
-    # Prepare the prompt for Google Gemini (extracted from OCR text)
     prompt = f"""
     Identify and extract manufacturing, expiration dates, and MRP from the following text.
     The dates may be written in dd/mm/yyyy format or as <Month_name> <Year> or <day> <Month_Name> <Year>.
@@ -25,9 +24,15 @@ async def extract_data(request: OCRText):
     Text: {request.ocr_text}. Reply with the extracted information only.
     """
     
-    # Call the Google Gemini model to generate the response based on the OCR input
     model = genai.GenerativeModel("gemini-1.5-flash")
     response = model.generate_content(prompt)
-
-    # Process the response and return it
     return {"message": response.text}
+
+# Gradio interface to call FastAPI logic
+def gradio_interface(ocr_text: str):
+    # FastAPI will handle this endpoint
+    response = extract_data(OCRText(ocr_text=ocr_text))
+    return response['message']
+
+iface = gr.Interface(fn=gradio_interface, inputs="text", outputs="json")
+iface.launch()

requirements.txt

@@ -1,5 +1,5 @@
 fastapi
-uvicorn[gunicorn]
 google-generativeai
 gradio
-pydantic
+uvicorn
+pydantic