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import base64
import json
import mimetypes
import re
from langchain.chat_models import init_chat_model
from langchain_core.messages import HumanMessage
model = init_chat_model("gemini-2.0-flash", model_provider="google_genai")
def _encode_file(file_path: str) -> str:
with open(file_path, "rb") as f:
return base64.b64encode(f.read()).decode("utf-8")
def extract_json(response: str) -> dict:
# Match inside ```json ... ```
match = re.search(r"```(?:json)?\s*({.*?})\s*```", response, re.DOTALL)
if match:
json_str = match.group(1)
else:
# Fallback if no code block
json_str = response.strip()
try:
return json.loads(json_str)
except json.JSONDecodeError as e:
return {"error": "Failed to parse JSON", "raw": response, "details": str(e)}
def analyze_media_structured(file_path: str) -> str:
"""
Analyze the uploaded image or video of a craft project.
Focus the analysis on general craft quality dimensions:
- Structure: Alignment, balance, symmetry
- Technique: Precision of folds, stitches, lines, etc.
- Neatness: Clean execution, absence of wrinkles or gaps
- Materials: Appropriate use, clarity, or consistency (if visible)
- Areas for improvement: Clear, actionable suggestions
Return a structured dictionary with specific observations.
Avoid general encouragement or introductions.
Do not refer to yourself or the tool.
"""
mime_type, _ = mimetypes.guess_type(file_path)
if not mime_type:
return {"error": "Unsupported file type."}
encoded = _encode_file(file_path)
prompt = f"""
You are a craft analysis assistant. The user uploaded a {mime_type} of their craft project.
Please return your observations on the following points:
- Structure: comment on balance, alignment, or symmetry.
- Technique: precision of folds, stitches, or construction.
- Neatness: how polished or clean the work appears.
- Materials: if visible, comment on appropriateness or consistency.
- Recommendations: a list of 2–3 actionable improvements.
Return the comments as a string.
"""
if mime_type.startswith("video"):
content = [
{"type": "text", "text": prompt},
{"type": "media", "data": encoded, "mime_type": mime_type},
]
elif mime_type.startswith("image"):
content = [
{"type": "text", "text": prompt},
{"type": "image_url", "image_url": f"data:{mime_type};base64,{encoded}"},
]
else:
return {"error": "Unsupported media type."}
response = model.invoke([HumanMessage(content=content)]).content
try:
return response
except Exception:
return "Failed to load response from media analysis" |