Update models/gpt_4.1.py

models/gpt_4.1.py

@@ -0,0 +1,116 @@
+# gpt4o_pix2struct_ocr.py
+
+import os
+import json
+import base64
+from PIL import Image
+import openai
+import torch
+
+model = "gpt-4.1"
+
+pix2struct_model = None
+processor = None
+
+
+def load_prompt(prompt_file="prompts/prompt.txt"):
+    #with open(prompt_file, "r", encoding="utf-8") as f:
+    #    return f.read().strip()
+    return os.getenv("PROMPT_TEXT", "⚠️ PROMPT_TEXT not found in secrets.")
+
+
+def try_extract_json(text):
+    try:
+        return json.loads(text)
+    except json.JSONDecodeError:
+        start = text.find('{')
+        if start == -1:
+            return None
+        brace_count = 0
+        json_candidate = ''
+        for i in range(start, len(text)):
+            if text[i] == '{':
+                brace_count += 1
+            elif text[i] == '}':
+                brace_count -= 1
+            json_candidate += text[i]
+            if brace_count == 0 and json_candidate.strip():
+                break
+        try:
+            return json.loads(json_candidate)
+        except json.JSONDecodeError:
+            return None
+
+
+def encode_image_base64(image: Image.Image):
+    from io import BytesIO
+    buffer = BytesIO()
+    image.save(buffer, format="JPEG")
+    return base64.b64encode(buffer.getvalue()).decode("utf-8")
+
+
+def extract_all_text_pix2struct(image: Image.Image):
+    global pix2struct_model, processor
+
+    # Lazy-load the Pix2Struct model
+    if processor is None or pix2struct_model is None:
+        from transformers import Pix2StructProcessor, Pix2StructForConditionalGeneration
+        processor = Pix2StructProcessor.from_pretrained("google/pix2struct-textcaps-base")
+        pix2struct_model = Pix2StructForConditionalGeneration.from_pretrained(
+            "google/pix2struct-textcaps-base"
+        ).to("cuda" if torch.cuda.is_available() else "cpu")
+
+    inputs = processor(images=image, return_tensors="pt").to(pix2struct_model.device)
+    predictions = pix2struct_model.generate(**inputs, max_new_tokens=512)
+    output_text = processor.decode(predictions[0], skip_special_tokens=True)
+    return output_text.strip()
+
+
+def assign_event_gateway_names_from_ocr(image: Image.Image, json_data, ocr_text):
+    if not ocr_text:
+        return json_data
+
+    def guess_name_fallback(obj):
+        if not obj.get("name") or obj["name"].strip() == "":
+            obj["name"] = "(label unknown)"
+
+    for evt in json_data.get("events", []):
+        guess_name_fallback(evt)
+
+    for gw in json_data.get("gateways", []):
+        guess_name_fallback(gw)
+
+    return json_data
+
+
+def run_model(image: Image.Image, api_key: str = None):
+    prompt_text = load_prompt()
+    encoded_image = encode_image_base64(image)
+
+    api_key = api_key or os.getenv("OPENAI_API_KEY")
+    if not api_key:
+        return {"json": None, "raw": "⚠️ API key is missing. Please set it as a secret in your Space or upload it as a file."}
+
+    client = openai.OpenAI(api_key=api_key)
+    response = client.chat.completions.create(
+        model=model,
+        messages=[
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": prompt_text},
+                    {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{encoded_image}"}}
+                ]
+            }
+        ],
+        max_tokens=5000
+    )
+
+    output_text = response.choices[0].message.content.strip()
+    parsed_json = try_extract_json(output_text)
+
+    # Use Pix2Struct OCR enrichment
+    full_ocr_text = extract_all_text_pix2struct(image)
+    parsed_json = assign_event_gateway_names_from_ocr(image, parsed_json, full_ocr_text)
+
+    return {"json": parsed_json, "raw": output_text}