single QCM prompt

app.py

@@ -1,9 +1,73 @@
 import gradio as gr
+import base64
 
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Function to encode the image
+def encode_image(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode("utf-8")
 
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+from openai import OpenAI
+
+client = OpenAI()
+
+SINGLE_QCM_PROMPT = """
+You are an assistant for a primary or secondary school teacher. 
+The attached image is a photo or screenshot of a multiple choice question found by the teacher in an exercise book. 
+Your job is to convert this image into a JSON file containing the question and proposed answers. 
+
+Let's proceed step by step:
+1. Start by identifying the question. Note that the question may be numbered with a digit (such as “1.”) or a letter (such as “a.”). This numbering is not part of the question.
+2. Identify all the proposed answers.
+3. Write the question and answers in a JSON file following the given example format.
+
+Example:
+- Input MCQ : "What year was America discovered? Answers: 1400, 1492, 1587, 1321"
+- Output JSON: {"Question": "What year was America discovered?", {"Answers": {"Answer":"1400"}, {"Answer":"1492"}, {"Answer":"1587"}, {"Answer":"1321"}}
+Answer only with the JSON file, don’t comment. Do not generate output that isn’t in properly formatted JSON. Respect the JSON format and especially the JSON keys “Question”,”Answers”,”Answer”.
+"""
+
+
+def process(image_path):
+    try:
+        response = client.chat.completions.create(
+            model="gpt-4o",
+            # response_format={ "type": "json_object" }, # si nécessaire
+            messages=[
+                # {"role": "system", "content": "You are a helpful assistant designed to output JSON."},
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": prompts.SINGLE_QCM_PROMPT},
+                        {
+                            "type": "image_url",
+                            "image_url": {
+                                "url": f"data:image/jpeg;base64,{encode_image(image_path)}"
+                            },
+                        },
+                    ],
+                }
+            ],
+            temperature=0.2,
+            #    max_tokens=256,
+            #    top_p=1,
+            #    frequency_penalty=0,
+            #    presence_penalty=0
+        )
+        return response.choices[0].message.content
+
+    except Exception as e:
+        print(f"an error occurred : {e}")
+        return {"error": str(e)}
+
+
+import gradio as gr
+
+iface = gr.Interface(
+    fn=process,
+    inputs=gr.Image(type="filepath"),
+    outputs=gr.JSON(),
+)
+
+iface.launch()