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from langchain_core.prompts import PromptTemplate
from langchain_groq import ChatGroq
from dotenv import load_dotenv
from fastapi import FastAPI, Response
from fastapi.middleware.cors import CORSMiddleware
from langchain_core.output_parsers import StrOutputParser
import pandas as pd
import uvicorn
import re
import os
load_dotenv()
app = FastAPI()
origins = ["*"]
app.add_middleware(
CORSMiddleware,
allow_origins=origins,
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"]
)
os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API_KEY")
program_df = pd.DataFrame({
"ProgramTitle": ["Champions League Highlights", "Legends Talk", "Classic Replays", "Morning Match Recap", "FanZone Live"],
"ProgramType": ["Highlights", "TalkShow", "Replay", "Digest", "TalkShow"],
"Duration": [60, 30, 90, 30, 60],
"TargetAudience": ["Males 18-35", "Females 25-40", "General", "All", "Males 18-50"],
"AvgRating": [8.2, 6.9, 7.4, 6.2, 7.8]
})
template = """
You are a sports TV schedule assistant.
Given the available program data and current day/time context, generate an optimal TV schedule.
🗓️ Context:
- Day: {day_of_week}
- Is Holiday: {is_holiday}
📋 Constraints:
- No back-to-back programs of the same `ProgramType`
- Prioritize highest-rated programs during prime time (18:00–22:00)
- No program can be repeated on the same day
🎥 Available Programs:
{program_list}
📝 OUTPUT FORMAT:
Return ONLY the final schedule in this format:
HH:MM - HH:MM ProgramName (ProgramType, Rating): <1-line reason>
⚠️ VERY IMPORTANT:
- Do NOT include any explanation, notes, or internal thinking
- Do NOT use tags like <think>, <plan>, <analyze>
- Do NOT return anything except the final schedule in the format provided above
- The output MUST start directly with the first time slot
"""
prompt = PromptTemplate.from_template(template)
llm = ChatGroq(model_name = "qwen-qwq-32b", api_key = os.environ["GROQ_API_KEY"])
chain = prompt | llm
@app.get("/api/v1/get_dynamic_schedule")
def get_dynamic_schedule():
try:
response = chain.invoke({
"day_of_week": "Saturday",
"is_holiday": "Yes",
"program_list": program_df.to_string(index=False)
})
text_data = response.content
if text_data and "</think>" in text_data:
result = re.split(r'</think>', text_data, maxsplit=1)[-1].strip()
return result
return response
except Exception as e:
return Response(content=str(e), media_type="text/markdown")
if __name__ == "__main__":
#get_dynamic_schedule()
uvicorn.run(app, host= "127.0.0.1", port= 8000)
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