adding apicall

api.py

@@ -0,0 +1,33 @@
+import os
+import requests
+
+# Environment variable for the token
+token = "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"
+endpoint = "https://models.github.ai/inference"
+model = "openai/gpt-4.1"
+
+def API_call(report):
+    headers = {
+        "Authorization": f"Bearer {token}",
+        "Content-Type": "application/json"
+    }
+
+    body = {
+        "messages": [
+            {"role": "system", "content": ""},
+            {"role": "user", "content": f"please structure this report into findings and impressions. Please be precise and just output the findings and impressions with no other text.{report}"}
+        ],
+        "temperature": 1,
+        "top_p": 1,
+        "model": model
+    }
+    
+    response = requests.post(f"{endpoint}/chat/completions", headers=headers, json=body)
+
+    if not response.ok:
+        raise Exception(response.json().get("error", "Unknown error"))
+
+    data = response.json()
+    return(data["choices"][0]["message"]["content"])
+
+

generate_report.py

@@ -6,9 +6,10 @@ import numpy as np
 from CNN_encoder import CNN_Encoder
 from distil_gpt2 import DistilGPT2
 from configs import argHandler
-from utils import load_image
+from utils import load_image, split_report_sections
 from tokenizer_wrapper import TokenizerWrapper
 from huggingface_hub import hf_hub_download
+from api import API_call
 # from src.models.cnn_encoder import 
 # from src.models.distil_gpt2 import DistilGPT2
 # from src.configs import argHandler
@@ -91,5 +92,8 @@ def generate_report(image_bytes):
     sentence = tokenizer_wrapper.GPT2_decode(tokens[0])
     sentence = tokenizer_wrapper.filter_special_words(sentence)
     print(sentence)
-    
-    return {"report": sentence}
+    # Call the API to structure the report
+    structured_report = API_call(sentence)
+    print(structured_report)
+    structured_report =split_report_sections(structured_report)
+    return structured_report

utils.py

@@ -5,7 +5,30 @@ import pydicom
 import torch
 from fastapi import HTTPException, UploadFile
 from skimage.transform import resize
+import re
 
+def clean_paragraph(text):
+    # Remove leading dashes, numbers, and extra whitespace
+    lines = text.strip().splitlines()
+    cleaned_lines = [re.sub(r"^\s*[-•\d.]*\s*", "", line) for line in lines if line.strip()]
+    return " ".join(cleaned_lines)
+
+def split_report_sections(report_text):
+    #strip any * in any place in the report
+    report_text = report_text.replace("*", "")
+    # Use regex to extract findings and impression sections
+    findings_match = re.search(r"(?i)findings:\s*(.*?)(?=(impressions?:))", report_text, re.DOTALL)
+    impression_match = re.search(r"(?i)impressions?:\s*(.*)", report_text, re.DOTALL)
+
+    findings_raw = findings_match.group(1).strip() if findings_match else ""
+    impression = impression_match.group(1).strip() if impression_match else ""
+
+    findings = clean_paragraph(findings_raw)
+
+    return {
+        "findings": findings,
+        "impression": impression  # Keep impression formatting as-is (or you can also clean it similarly)
+    }
 
 def load_image(image):
     image = image.convert("RGB")