fixing flake8 errors

chat_doc/dataset_generation/dataset_factory.py

@@ -39,7 +39,6 @@ def build_full_dialogue_dataset(self):
             logger.error(e)
 
         return prompts
-
 
     def build_full_dataset(self):
         # load both datasets, if they don't exist, build them
@@ -69,7 +68,7 @@ def build_full_dataset(self):
             logger.error(e)
 
         return prompts
-    
+
     def load_full_dialogue_dataset(self):
         try:
             with open(self.dialogue_path, "rb") as f:

chat_doc/dataset_generation/med_dialogue_dataset.py

@@ -20,7 +20,7 @@ def __init__(self, name="med_dialogue"):
 
     def load_data(self: str):
         """
-        Load Medical Dialogue data from HF dataset. 
+        Load Medical Dialogue data from HF dataset.
         """
         try:
             raw_dataset = load_dataset(self.med_dialogue_hf_id, self.variant)
@@ -58,13 +58,13 @@ def build_prompts(self):
             med_dialogue = self.dataset.copy()
 
         prompts = []
-        for _, row in tqdm(med_dialogue.iterrows(), total=med_dialogue.shape[0]):                  
+        for _, row in tqdm(med_dialogue.iterrows(), total=med_dialogue.shape[0]):
 
             prompts.append(
                 # inherit from ChatDataset
                 self.unify_prompt(
                     instruction=f"{row['patient']}",
-                    context=f"",
+                    context="",
                     response=f"{row['doctor']}",
                 )
             )

chat_doc/dataset_generation/pmc_patients_dataset.py

@@ -81,35 +81,34 @@ def _str_sex(sex_str: str) -> str:
         logger.info("PMC patients prompts built.")
         self.prompts = prompts
 
-
     def build_prompts(self):
         if self._is_processed():
             pmc_data = self.dataset.copy()
 
         def _str_sex(sex_str: str) -> str:
             return "male" if sex_str == "M" else "female"
-        
+
         # Sample prompt templates
-        templates = [
-            f"Summarize the medical history for a {age}-year-old {gender} patient based on the following summary: {patient_summary}",
-            f"Explain the treatment options for a patient with this profile: {patient_summary}",
-            f"Identify potential diagnoses for a {age}-year-old patient presenting these symptoms: {patient_summary}"
-        ]
+        # templates = [
+        #     "Summarize the medical history for a {age}-year-old {gender} patient based on the following summary: {patient_summary}",
+        #     "Explain the treatment options for a patient with this profile: {patient_summary}",            
+        # ]
 
         prompts = []
-        for _, row in pmc_data.iterrows():            
-            template = np.random.choice(templates)
+        for _, row in pmc_data.iterrows():
+            # template = np.random.choice(templates)            
             age = row.age
-            gender = _str_sex(row['sex'])
+            sex = _str_sex(row['sex'])
             patient_summary = row['patient']
+            instruction_template = f"Identify potential diagnoses for a {age}-year-old {sex} patient presenting these symptoms: {patient_summary}"
 
             prompts.append(
                 # inherit from ChatDataset
                 self.unify_prompt(
-                    instruction=template.format(age=age, gender=gender, patient_summary=patient_summary),
+                    instruction=instruction_template,
                     context="",
                     response=f"{patient_summary}",
                 )
-            )            
+            )
 
         return prompts

chat_doc/inference/chat.py

@@ -1,24 +1 @@
-from sagemaker.huggingface.model import HuggingFaceModel
-
-# create Hugging Face Model Class
-huggingface_model = HuggingFaceModel(
-   model_data="s3://models/my-bert-model/model.tar.gz",  # path to your trained SageMaker model
-   role=role,                                            # IAM role with permissions to create an endpoint
-   transformers_version="4.26",                           # Transformers version used
-   pytorch_version="1.13",                                # PyTorch version used
-   py_version='py39',                                    # Python version used
-)
-
-# deploy model to SageMaker Inference
-predictor = huggingface_model.deploy(
-   initial_instance_count=1,
-   instance_type="ml.m5.xlarge"
-)
-
-# example request: you always need to define "inputs"
-data = {
-   "inputs": "Camera - You are awarded a SiPix Digital Camera! call 09061221066 fromm landline. Delivery within 28 days."
-}
-
-# request
-predictor.predict(data)
+# see inference.ipynb for now