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import os
import requests
from datasets import load_dataset
from sentence_transformers import SentenceTransformer
import numpy as np
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from dotenv import load_dotenv
load_dotenv()
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
dataset = load_dataset("altaidevorg/women-health-mini")
conversation_data = [
turn["content"]
for conv in dataset["train"]
for turn in conv["conversations"]
]
model = SentenceTransformer("all-MiniLM-L6-v2", device="cpu")
conversation_embeddings = model.encode(conversation_data)
GROQ_API_URL = "https://api.groq.com/openai/v1/chat/completions"
def get_more_relevant_rsponse(query):
query_embedding = model.encode([query])
similarities = np.dot(conversation_embeddings, query_embedding.T).flatten()
best_match_idx = np.argmax(similarities)
return conversation_data[best_match_idx]
@app.post("/chat/")
async def chat_with_bot(user_query:dict):
prompt = user_query.get("message", "")
if not prompt :
return {"response": "Prompt is required!"}
history_response = get_more_relevant_rsponse(prompt)
context_prompt = f"""
You are a chatbot named fille AI specialized in women's health. Provide **clear, factual, and supportive** responses.
If the user's question involves medical advice, remind them to consult a healthcare professional.
User Question: {prompt}
Below is a similar question/response from the knowledge base that may contain helpful information:
{history_response}
Please provide your own professional, friendly, and informative response that addresses the user's specific question.
"""
headers = {
"Authorization": f"Bearer {GROQ_API_KEY}",
"Content-Type": "application/json"
}
payload = {
"model": "llama3-70b-8192",
"messages": [{"role": "user", "content": context_prompt}]
}
response = requests.post(GROQ_API_URL, json=payload, headers=headers)
if response.status_code == 200:
bot_reply = response.json()["choices"][0]["message"]["content"]
return {
"response": bot_reply
}
else:
return {"response": "Error in fetching the data from groq AI"}
if __name__ == "__main__" :
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000) |