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from fastapi import FastAPI
from pydantic import BaseModel
from groq import Groq
import os
import logging
#from dotenv import load_dotenv
#load_dotenv()
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("uvicorn")
# Initialize the FastAPI app
app = FastAPI()
# Get the API key from an environment variable
groq_api_key = os.getenv("GROQ_API_KEY")
if not groq_api_key:
raise ValueError("GROQ_API_KEY environment variable is not set")
# Initialize Groq client
client = Groq(api_key=groq_api_key)
# Define the system message
System_msg = '''-act as an experienced blockchain developer, working for 15 years.
-help me understand some concepts, assume I am a complete beginner.
-If the user asks anything not related to blockchain, just say you don't know about it.'''
# Request model for FastAPI
class ChatRequest(BaseModel):
message: str
history: list
@app.get("/.")
def read_root():
return {"message": "FastAPI is running!"}
# FastAPI chat endpoint
@app.post("/chat")
def chat(request: ChatRequest):
logging.info(f"Received request: {request}")
message = request.message
history = request.history
# Create the history_list to send to the Groq API
history_list = [{"role": "system", "content": System_msg}]
for human, ai in history:
history_list.append({"role": "user", "content": human})
history_list.append({"role": "assistant", "content": ai})
# Append the new user message to the history
history_list.append({"role": "user", "content": message})
# Try to get the response from the LLaMA API (Groq)
try:
response = client.chat.completions.create(
model="llama-3.1-70b-versatile", # Ensure the correct model name
messages=history_list,
temperature=1.0,
max_tokens=4000,
stream=False # Use streaming for real-time responses
)
final_message = response.choices[0].message.content
# Return the final AI-generated message
return {"response": final_message}
except Exception as e:
return {"response": f"Error: {str(e)}"}
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