Create main.py

main.py

@@ -0,0 +1,36 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import GPT2Tokenizer, GPT2Model
+from langchain.prompts import PromptTemplate
+
+app = FastAPI()
+
+tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+model = GPT2Model.from_pretrained('gpt2')
+
+
+class TextRequest(BaseModel):
+    text: str
+
+
+def preprocess_text(text: str):
+    return text.lower()
+
+
+def classify_text(question: str):
+    prompt_template = PromptTemplate(template="Answer the following question and classify it: {question}", input_variables = ["question"], output_variables=["answer", "classification"])
+    # Model loading
+    format_prompt = prompt_template.format(question=question)
+    encoded_input = tokenizer(format_prompt, return_tensors='pt')
+    output = model(encoded_input)
+    # chain = LLMChain(llm=llm, prompt=prompt_template, verbose=True)
+    # response = chain({"question": question})
+    return output
+
+
+@app.post("/classify")
+async def classify_text_endpoint(request: TextRequest):
+    preprocessed_text = preprocess_text(request.text)
+    response = classify_text(preprocessed_text)
+    answer = response['text']
+    return {"answer": answer}