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c001f24 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 | import os
import json
import requests
import sys
import base64
from typing import List, Optional, Dict, Any
def generate_subjective_questions(image_path: str) -> Optional[Dict[Any, Any]]:
"""
Transcribes and structures subjective questions from an image using the Gemini API.
"""
api_key = os.environ.get("GEMINI_API_KEY")
if not api_key:
print("Error: GEMINI_API_KEY environment variable is not set.", file=sys.stderr)
return None
# Read and encode image
try:
with open(image_path, "rb") as image_file:
encoded_string = base64.b64encode(image_file.read()).decode('utf-8')
except Exception as e:
print(f"Error reading image file: {e}", file=sys.stderr)
return None
model_id = "gemini-flash-latest"
url = f"https://generativelanguage.googleapis.com/v1beta/models/{model_id}:generateContent?key={api_key}"
headers = {'Content-Type': 'application/json'}
prompt_text = """
Analyze the provided image. It contains a list of subjective questions (handwritten or printed).
Task:
1. **Transcribe** each question exactly as written.
2. **Identify the Topic:** Determine the subject or topic for each question (e.g., "Ascomycetes", "Thermodynamics"). If the header specifies a topic, use that.
3. **Structure:** Return the data in the specified JSON format.
4. **Numbering:** Use the question number found in the image.
If the image contains multiple questions, extract all of them.
"""
request_body = {
"contents": [
{
"role": "user",
"parts": [
{
"inline_data": {
"mime_type": "image/jpeg", # Assuming JPEG/PNG, API is flexible with image/* usually, but let's send jpeg or png based on file if needed, usually jpeg works for generic
"data": encoded_string
}
},
{
"text": prompt_text
}
]
}
],
"generationConfig": {
"responseMimeType": "application/json",
"responseSchema": {
"type": "object",
"properties": {
"success": {"type": "boolean"},
"data": {
"type": "array",
"items": {
"type": "object",
"properties": {
"question_topic": {"type": "string"},
"question_html": {"type": "string"},
"question_number_within_topic": {"type": "string"}
},
"required": ["question_topic", "question_html", "question_number_within_topic"]
}
}
},
"required": ["success", "data"]
}
}
}
try:
response = requests.post(url, headers=headers, json=request_body, timeout=120)
response.raise_for_status()
response_json = response.json()
# Extract text from candidate
candidate = response_json.get('candidates', [{}])[0]
content = candidate.get('content', {})
parts = content.get('parts', [])
if not parts:
print("Error: Gemini generated no content.")
return None
text = parts[0]['text']
return json.loads(text)
except requests.exceptions.RequestException as e:
print(f"Error during Gemini API call: {e}", file=sys.stderr)
if e.response:
print(f"Response: {e.response.text}", file=sys.stderr)
return None
except json.JSONDecodeError as e:
print(f"Error parsing JSON response: {e}", file=sys.stderr)
print(f"Raw text: {text}", file=sys.stderr)
return None
if __name__ == "__main__":
# Test the function
result = generate_subjective_questions("Ascomycetes")
if result:
print(json.dumps(result, indent=2))
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