main.py

from fastapi import FastAPI, HTTPException, Request, Query
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from typing import List, Dict, Any
from helper_functions_api import md_to_html, search_brave, fetch_and_extract_content, limit_tokens, together_response, insert_data
import os
from dotenv import load_dotenv, find_dotenv
from datetime import datetime, timedelta
from fastapi_cache import FastAPICache
from fastapi_cache.backends.inmemory import InMemoryBackend
from fastapi_cache.decorator import cache
import asyncio
import re
# Load environment variables from .env file
#load_dotenv("keys.env")

app = FastAPI()

@app.on_event("startup")
async def startup():
    FastAPICache.init(InMemoryBackend(), prefix="fastapi-cache")
    
TOGETHER_API_KEY = os.getenv('TOGETHER_API_KEY')
BRAVE_API_KEY = os.getenv('BRAVE_API_KEY')
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
HELICON_API_KEY = os.getenv("HELICON_API_KEY")
SUPABASE_USER = os.environ['SUPABASE_USER']
SUPABASE_PASSWORD = os.environ['SUPABASE_PASSWORD']

# Groq model names
llm_default_small = "llama3-8b-8192"
llm_default_medium = "llama3-70b-8192"

# Together Model names (fallback)
llm_fallback_small = "meta-llama/Llama-3-8b-chat-hf"
llm_fallback_medium = "meta-llama/Llama-3-70b-chat-hf"

SysPromptJson = "You are now in the role of an expert AI who can extract structured information from user request. Both key and value pairs must be in double quotes. You must respond ONLY with a valid JSON file. Do not add any additional comments."
SysPromptList = "You are now in the role of an expert AI who can extract structured information from user request. All elements must be in double quotes. You must respond ONLY with a valid python List. Do not add any additional comments."
SysPromptDefault = "You are an expert AI, complete the given task. Do not add any additional comments."
SysPromptMd = "You are an expert AI who can create a structured report using information provided in the context from user request.The report should be in markdown format consists of markdown tables structured into subtopics. Do not add any additional comments."

prompt_user = {}
prompt_system = {}
prompt_user["online"] = {}
prompt_user["offline"] = {}
prompt_user["online"]["chat"] = "Write a well thought out answer to the query:: {description} #### , refer the provided internet search results reference:{reference}"
prompt_user["online"]["report"] = "Write a well thought out Report to the query:: {description} #### , refer the provided internet search results reference:{reference}"
prompt_user["online"]["report_table"] = "Write a well thought out Report to the query:: {description}, include any relevant tables if present in the source #### , refer the provided internet search results reference:{reference}"

prompt_user["offline"]["chat"] = "Write a well thought out, detailed and structured answer to the query:: {description}"
prompt_user["offline"]["report"] = "Write a well thought out, detailed and structured Report to the query:: {description}"
prompt_user["offline"]["report_table"] = "Write a detailed and structured Report to the query:: {description}, The report should be in markdown format structured into subtopics/tables/lists"

prompt_system["online"] = """You are an expert AI who can create detailed answers using internet search results.
                                1 filter and summarize relevant information, if there are conflicting information, use the latest source.
                                2. use it to construct a clear and factual answer.
                                Your response should be properly formatted and well readable using markdown formatting. Ignore Links"""

prompt_system["offline"] = """You are an expert AI who can create detailed answers. Your response should be properly formatted and well readable using markdown formatting."""

# sys_prompts = {
#     "offline": {
#         "Chat": "You are an expert AI, complete the given task. Do not add any additional comments.",
#         "Full Text Report": "You are an expert AI who can create a detailed report from user request. The report should be in markdown format. Do not add any additional comments.",
#         "Tabular Report": "You are an expert AI who can create a structured report from user request.The report should be in markdown format structured into subtopics/tables/lists. Do not add any additional comments.",
#         "Tables only": "You are an expert AI who can create a structured tabular report from user request.The report should be in markdown format consists of only markdown tables. Do not add any additional comments.",
#     },
#     "online": {
#         "Chat": "You are an expert AI, complete the given task using the provided context. Do not add any additional comments.",
#         "Full Text Report": "You are an expert AI who can create a detailed report using information scraped from the internet. You should decide which information is relevant to the given task and use it to create a report. The report should be in markdown format. Do not add any additional comments.",
#         "Tabular Report": """You are an expert AI who can provide answers using internet search results.
#                                 1 filter and summarize relevant information, if there are conflicting information, use the latest source.
#                                 2. use it to construct a clear and factual answer.
#                                 Your response should be properly formatted and well readable using markdown formatting. """,
#         "Tables only": "You are an expert AI who can create a structured tabular report using information scraped from the internet. You should decide which information is relevant to the given task. The report should be in markdown format consists of only markdown tables. Do not add any additional comments.",
#     },
# }


class QueryModel(BaseModel):
    topic: str = Query(default="market research", description="input query to generate Report")
    description: str = Query(default="", description="additional context for report")
    user_id: str = Query(default="", description="unique user id")
    user_name: str = Query(default="", description="user name")
    internet: bool = Query(default=True, description="Enable Internet search")
    output_format: str = Query(default="report_table", description="Output format for the report",
                               enum=["chat", "report", "report_table"])
    data_format: str = Query(default="Structured data", description="Type of data to extract from the internet",
                             enum=["No presets", "Structured data", "Quantitative data"])

@cache(expire=604800)
async def generate_report(query: QueryModel):
    query_str = query.topic
    description = query.description
    user_id = query.user_id
    internet = "online" if query.internet else "offline"
    user_prompt_final = prompt_user[internet][query.output_format]
    system_prompt_final = prompt_system[internet]
    data_format = query.data_format
    optimized_search_query = ""
    all_text_with_urls = [("", "")]

    if query.internet:
        search_query = re.sub(r'[^\w\s]', '', description).strip()
        try:
            urls, optimized_search_query = search_brave(search_query, num_results=8)
            all_text_with_urls = fetch_and_extract_content(data_format, urls, query_str)
            reference = limit_tokens(str(all_text_with_urls))
            user_prompt_final = user_prompt_final.format(description=description, reference=reference)
        except Exception as e:
            print(e)
            query.internet = False
            print("failed to search/scrape results, falling back to LLM response")

    if not query.internet:
        user_prompt_final = prompt_user["offline"][query.output_format].format(description=description)
        system_prompt_final = prompt_system["offline"]

    md_report = together_response(user_prompt_final, model=llm_default_medium, SysPrompt=system_prompt_final)

    if user_id != "test":
        insert_data(user_id, query_str, description, str(all_text_with_urls), md_report)
    references_html = {}
    for text, url in all_text_with_urls:
        references_html[url] = str(md_to_html(text))

    return {
        "report": md_to_html(md_report),
        "references": references_html,
        "search_query": optimized_search_query
    }

@app.post("/generate_report")
async def api_generate_report(request: Request, query: QueryModel):
    return await generate_report(query)
    
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],)