adding __main__py file

__main__.py

@@ -0,0 +1,89 @@
+from transformers import  Pipeline, AutoTokenizer
+import torch
+from bs4 import BeautifulSoup
+import re
+
+class MyPipeline(Pipeline):
+    tokenizer = AutoTokenizer.from_pretrained("abacusai/Llama-3-Smaug-8B")
+    def _sanitize_parameters(self, **kwargs):
+        preprocess_kwargs = {}
+        if "context" in kwargs:
+            preprocess_kwargs["context"] = kwargs["context"]
+        if "search_person" in kwargs:
+            preprocess_kwargs["search_person"] = kwargs["search_person"]
+        return preprocess_kwargs, {}, {}
+
+    def preprocess(self, inputs, **kwargs):
+        tokenizer = MyPipeline.tokenizer
+        context = inputs["context"]
+        search_person = inputs["search_person"]
+        #print(f"here -->  {len(context)},   {search_person}")
+
+        def create_prompt(context, search_person):
+            def clean_text(text):
+                soup = BeautifulSoup(text, 'html.parser')
+                for link in soup.find_all('a'):
+                    link.decompose()
+                text = re.sub(r'\([^)]*\)', '', soup.get_text())
+                return text
+
+            def prepare_question(search_person):
+                q = f"""
+                Based on the information provided in the context, what is the most likely perception of {search_person}?
+                Pick one answer option.
+
+                Answer options:
+
+                Positive: {search_person} is portrayed in a favorable light, and the context suggests that she is a caring and responsible parent.
+                Negative: {search_person} is portrayed in an unfavorable light, and the context suggests that she is a neglectful and/or abusive parent.
+                Neutral: The context does not provide enough information to make a determination about the character or actions of {search_person}, or it presents a balanced and unbiased view of her.
+                """
+                return q
+
+            context = clean_text(context)
+            question = prepare_question(search_person)
+            if len(tokenizer.tokenize(context + ' ' + question)) > tokenizer.model_max_length:
+                print("found such")
+                context = context[500:]
+
+            prompt_template = f"### CONTEXT\n{context}\n\n### QUESTION\n{question}\n\n### ANSWER\n"
+            return prompt_template
+
+        prompt = create_prompt(context, search_person)
+        predToken = tokenizer(prompt, return_tensors='pt')
+        #tokens = self.tokenizer(prompt, return_tensors='pt')
+        
+        return predToken
+
+    def _forward(self, model_inputs):
+        tokenizer = MyPipeline.tokenizer
+        try:
+          # out of memory error is most likely to happen here
+          device = "cuda" if torch.cuda.is_available() else "cpu"
+          self.model = self.model.to(device)
+          model_inputs = {k:v.to(device) for k,v in model_inputs.items()}
+        except RuntimeError as e:
+          # explicitly transferring to cpu
+          self.model = self.model.to("cpu")
+          model_inputs = {k:v.to("cpu") for k,v in model_inputs.items()}
+        #model_inputs = model_inputs.to(device)
+
+        with torch.no_grad():
+            outputs = self.model.generate(**model_inputs, max_new_tokens=20,pad_token_id=tokenizer.eos_token_id)
+        generated_tokens = outputs[0, len(model_inputs['input_ids'][0]):]
+        out_text = tokenizer.decode(generated_tokens, skip_special_tokens=True).strip()
+        return {'out_text': out_text}
+
+    def postprocess(self, model_outputs):
+        out_text = model_outputs['out_text']
+        if 'Positive' in out_text:
+            return 'Positive'
+        elif 'Negative' in out_text:
+            return 'Negative'
+        elif 'Neutral' in out_text:
+            return 'Neutral'
+        else:
+            return 'Neutral'
+
+# Initialize the model and tokenizer
+