app.py

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from sentence_transformers import SentenceTransformer, util
from transformers import pipeline
from transformers import T5Tokenizer, T5ForConditionalGeneration


# Initialize FastAPI app
app = FastAPI()

# Load models
model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
question_model = "deepset/tinyroberta-squad2"
nlp = pipeline('question-answering', model=question_model, tokenizer=question_model)

t5tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-large")
t5model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large")

# Define request models
class ModifyQueryRequest(BaseModel):
    query_string: str

class AnswerQuestionRequest(BaseModel):
    question: str
    context: list
    locations: list

class T5QuestionRequest(BaseModel):
    question: str
    context: str

class T5Response(BaseModel):
    answer: str

# Define response models (if needed)
class ModifyQueryResponse(BaseModel):
    embeddings: list

class AnswerQuestionResponse(BaseModel):
    answer: str
    locations: list

# Define API endpoints
@app.post("/modify_query", response_model=ModifyQueryResponse)
async def modify_query(request: ModifyQueryRequest):
    try:
        binary_embeddings = model.encode([request.query_string], precision="binary")
        return ModifyQueryResponse(embeddings=binary_embeddings[0].tolist())
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/answer_question", response_model=AnswerQuestionResponse)
async def answer_question(request: AnswerQuestionRequest):
    try:
        res_locs = []
        context_string = ''
        corpus_embeddings = model.encode(request.context, convert_to_tensor=True)
        query_embeddings = model.encode(request.question, convert_to_tensor=True)
        hits = util.semantic_search(query_embeddings, corpus_embeddings)
        for hit in hits[0]:
            if hit['score'] > .4:
                loc = hit['corpus_id']
                res_locs.append(request.locations[loc])
                context_string += request.context[loc] + ' '
        if len(res_locs) == 0:
            ans = "Sorry, I couldn't find any results for your query. Please try again!"
        else:
            QA_input = {
                'question': request.question,
                'context': context_string.replace('\n',' ')
            }
            result = nlp(QA_input)
            ans = result['answer']
        return AnswerQuestionResponse(answer=ans, locations = res_locs)
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/t5answer", response_model=T5Response)
async def t5answer(request: T5QuestionRequest):
    input_text = request.question + ":" + request.context
    input_ids = t5tokenizer(input_text, return_tensors="pt").input_ids
    outputs = t5model.generate(input_ids)
    resp = t5tokenizer.decode(outputs[0], skip_special_tokens=True)
    return T5Response(answer = resp)

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)