added customer conversation

app.py

@@ -2,6 +2,7 @@ from fastapi import FastAPI
 from pydantic import BaseModel
 from calculator import calculate
 from sentimentAnalysis import sentimentAnalysis
+from customerSupport import customerConverstaion
 
 
 class User_input(BaseModel):
@@ -20,14 +21,19 @@ def greet_json():
 
 
 @app.post("/calculate")
-def operate(input:User_input):
+def calculate(input:User_input):
     res= calculate(input.operation, input.x, input.y)
     return res
 
 @app.post("/sentimentAnalysis")
-def operate(input:User_input):
+def sentimentAnalysis(input:User_input):
     res= sentimentAnalysis(input.sentence)
     return res
 
+@app.post("/getReply")
+def getReply(input:User_input):
+    res= customerConverstaion(input.sentence)
+    return res
+
 
 

customerSupport.py

@@ -0,0 +1,41 @@
+
+from transformers import AutoModelForCausalLM,GenerationConfig
+from peft import AutoPeftModelForCausalLM
+from peft import PeftModel, PeftConfig
+
+def input_data_preprocessing(example):
+
+    processed_example = "<|system|>\n You are a support chatbot who helps with user queries chatbot who always responds in the style of a professional.\n<|user|>\n" + example["instruction"] + "\n<|assistant|>\n"
+
+    return processed_example
+
+
+
+def customerConverstaion(prompt):
+    config = PeftConfig.from_pretrained("DSU-FDP/customer-support")
+    base_model = AutoModelForCausalLM.from_pretrained("TheBloke/zephyr-7B-beta-GPTQ")
+    model = PeftModel.from_pretrained(base_model, "DSU-FDP/customer-support")
+    from transformers import AutoTokenizer,GPTQConfig
+    tokenizer=AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
+    tokenizer.padding_side = 'right'
+    tokenizer.pad_token = tokenizer.eos_token
+    tokenizer.add_eos_token = True
+    tokenizer.add_bos_token, tokenizer.add_eos_token
+    tokenizer = AutoTokenizer.from_pretrained("DSU-FDP/customer-support")
+    input_string = input_data_preprocessing(
+    {
+        "instruction": "i have a question about cancelling order {{Order Number}}",
+    }
+    )
+    inputs = tokenizer(input_string, return_tensors="pt").to("cuda")
+    generation_config = GenerationConfig(
+        do_sample=True,
+        top_k=1,
+        temperature=0.1,
+        max_new_tokens=256,
+        pad_token_id=tokenizer.eos_token_id
+    )
+    outputs = model.generate(**inputs, generation_config=generation_config)
+    return outputs
+
+

requirements.txt

@@ -3,3 +3,4 @@ torch
 transformers
 pydantic
 uvicorn[standard]
+peft