more efficiewnt backend

Dockerfile

@@ -1,28 +1,19 @@
-# This is the single Dockerfile for our entire backend on Hugging Face Spaces
 FROM python:3.11-slim
 
-# Set a single working directory
 WORKDIR /app
 
-# Install system dependencies
 RUN apt-get update && apt-get install -y git redis-server
 
-# Copy all requirements and model files first for better caching
 COPY backend/requirements.txt .
 COPY ml_models ./ml_models
 
-# Install Python packages
 RUN pip install --no-cache-dir -r requirements.txt
 
-# Copy the entire backend source code
 COPY backend .
 
-# Create a startup script
 COPY startup.sh .
 RUN chmod +x startup.sh
 
-# Expose the port FastAPI will run on
 EXPOSE 7860
 
-# The command to run our startup script
 CMD ["./startup.sh"]

backend/Dockerfile

@@ -14,43 +14,3 @@ WORKDIR /code/app
 
 
 
-# FROM python:3.11-slim
-
-# WORKDIR /code
-
-# RUN apt-get update && apt-get install -y git
-
-# COPY ./backend/requirements.txt .
-# RUN pip install --no-cache-dir --upgrade -r requirements.txt
-
-# COPY ./ml_models /code/sentiment_model
-
-# WORKDIR /code/app
-
-# # This is the default command for our web server
-# CMD ["python", "-m", "uvicorn", "main:app", "--host", "0.0.0.0", "--port", "10000"]
-
-
-
-
-
-
-# FROM python:3.11-slim
-
-# WORKDIR /code
-
-# RUN apt-get update && apt-get install -y git
-
-# COPY ./backend/requirements.txt .
-# # Install Gunicorn for a production-ready server
-# RUN pip install gunicorn
-# RUN pip install --no-cache-dir -r requirements.txt
-
-# COPY ./ml_models /code/sentiment_model
-
-# # Copy the application code last. All code will live in /code now.
-# COPY ./backend .
-
-# # The default command is to start the web server.
-# # Render's free web services require port 10000.
-# CMD ["gunicorn", "-w", "2", "-k", "uvicorn.workers.UvicornWorker", "main:app", "--bind", "0.0.0.0:10000"]

backend/celery_worker.py

@@ -5,7 +5,6 @@ celery = Celery(
     "quantitative_analysis_platform",
     broker=settings.CELERY_BROKER_URL,
     backend=settings.CELERY_RESULT_BACKEND,
-    # This is the corrected list. We only have one task file now.
     include=[
         "tasks.main_task"
     ]

backend/core/config.py

@@ -1,31 +1,16 @@
-# from pydantic_settings import BaseSettings, SettingsConfigDict
-
-# class Settings(BaseSettings):
-#     DATABASE_URL: str
-#     CELERY_BROKER_URL: str
-#     CELERY_RESULT_BACKEND: str
-#     GOOGLE_API_KEY: str
-
-#     model_config = SettingsConfigDict(env_file=".env")
-
-# settings = Settings()
 
 
 
 from pydantic_settings import BaseSettings, SettingsConfigDict
 
 class Settings(BaseSettings):
-    # These variables will now be loaded from the Hugging Face secrets UI
     DATABASE_URL: str
     GOOGLE_API_KEY: str
-    
-    # These variables are hardcoded for the Hugging Face environment
-    # because Redis is running in the same container.
+
     CELERY_BROKER_URL: str = "redis://localhost:6379/0"
     CELERY_RESULT_BACKEND: str = "redis://localhost:6379/0"
 
-    # This tells Pydantic to first look for system environment variables,
-    # and then fall back to a .env file if one exists.
+
     model_config = SettingsConfigDict(env_file=".env", env_file_encoding='utf-8', extra='ignore')
 
 settings = Settings()

backend/main.py

@@ -1,71 +1,3 @@
-# from fastapi import FastAPI, Depends, HTTPException
-# from fastapi.middleware.cors import CORSMiddleware
-# from sqlalchemy.orm import Session
-# from sqlalchemy import desc # Import desc for ordering
-# from uuid import UUID
-# from typing import List # Import List for the history endpoint
-# import models.analysis_job as model
-# import schemas
-# from core.database import SessionLocal, engine
-# from tasks.main_task import run_full_analysis
-
-# model.Base.metadata.create_all(bind=engine)
-
-# app = FastAPI(
-#     title="Quantitative Analysis Platform API",
-#     version="0.1.0",
-# )
-
-# app.add_middleware(
-#     CORSMiddleware,
-#     allow_origins=["*"],
-#     allow_credentials=True,
-#     allow_methods=["*"],
-#     allow_headers=["*"],
-# )
-
-# def get_db():
-#     db = SessionLocal()
-#     try:
-#         yield db
-#     finally:
-#         db.close()
-
-# @app.post("/jobs", response_model=schemas.Job, status_code=201)
-# def create_analysis_job(job_request: schemas.JobCreate, db: Session = Depends(get_db)):
-#     db_job = model.AnalysisJob(ticker=job_request.ticker.upper())
-#     db.add(db_job)
-#     db.commit()
-#     db.refresh(db_job)
-    
-#     run_full_analysis.delay(str(db_job.id), db_job.ticker)
-    
-#     return db_job
-
-# @app.get("/jobs/{job_id}", response_model=schemas.Job)
-# def get_job_status(job_id: UUID, db: Session = Depends(get_db)):
-#     db_job = db.query(model.AnalysisJob).filter(model.AnalysisJob.id == job_id).first()
-#     if db_job is None:
-#         raise HTTPException(status_code=404, detail="Job not found")
-#     return db_job
-
-# # --- NEW ENDPOINT FOR HISTORY PANEL ---
-# @app.get("/jobs", response_model=List[schemas.Job])
-# def get_jobs_history(db: Session = Depends(get_db)):
-#     db_jobs = db.query(model.AnalysisJob).order_by(desc(model.AnalysisJob.created_at)).limit(20).all()
-#     return db_jobs
-
-
-
-
-
-
-
-
-
-
-
-
 from fastapi import FastAPI, Depends, HTTPException
 from fastapi.middleware.cors import CORSMiddleware
 from sqlalchemy.orm import Session
@@ -84,9 +16,6 @@ app = FastAPI(
     version="0.1.0",
 )
 
-# --- THIS IS THE FINAL FIX ---
-# This configuration allows your Vercel app and all its preview deployments
-# to communicate with the backend, as well as your local development server.
 app.add_middleware(
     CORSMiddleware,
     allow_origin_regex=r"https?://.*\.vercel\.app|http://localhost:5173",
@@ -94,7 +23,6 @@ app.add_middleware(
     allow_methods=["*"],
     allow_headers=["*"],
 )
-# --- END OF FIX ---
 
 def get_db():
     db = SessionLocal()

backend/models/analysis_job.py

@@ -1,18 +1,3 @@
-# from sqlalchemy import Column, String, JSON
-# from sqlalchemy.dialects.postgresql import UUID
-# import uuid
-# from core.database import Base
-
-# class AnalysisJob(Base):
-#     __tablename__ = "analysis_jobs"
-
-#     id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
-#     ticker = Column(String, nullable=False, index=True)
-#     status = Column(String, default="PENDING", nullable=False)
-#     result = Column(JSON, nullable=True)
-
-
-
 from sqlalchemy import Column, String, JSON, DateTime
 from sqlalchemy.dialects.postgresql import UUID
 import uuid

backend/requirements.txt

@@ -1,35 +1,27 @@
-# FastAPI and Server
 fastapi
 uvicorn[standard]
 gunicorn
 pydantic-settings
 
-# Database
 sqlalchemy
 psycopg2-binary
 alembic
 
-# Task Queue
 celery
 redis
 
-# --- CORE DATA SCIENCE LIBS (PINNING NUMPY) ---
-numpy<2.0 # CRITICAL FIX: Pin numpy to a version compatible with pandas-ta
-pandas
+numpy<2.0 
 pandas-ta
 matplotlib
 
-# Data Agent & Prediction
 yfinance
 
-# Intelligence Agent
 newspaper3k
 lxml_html_clean
 snscrape@git+https://github.com/JustAnotherArchivist/snscrape.git@master
 requests
 beautifulsoup4
 
-# AI / ML / LLM
 torch
 transformers
 sentence-transformers

backend/tasks/analyst_tasks.py

@@ -1,110 +1,3 @@
-# from celery_worker import celery
-# from core.database import SessionLocal
-# from models.analysis_job import AnalysisJob
-# from tools.analyst_tools import get_llm_analysis
-# from uuid import UUID
-
-# @celery.task
-# def run_llm_analysis(job_id: str):
-#     db = SessionLocal()
-#     job = None
-#     try:
-#         job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first()
-#         if not job or not job.result:
-#             raise ValueError("Job not found or has no initial data.")
-
-#         job.status = "ANALYZING" # New status for the frontend
-#         db.commit()
-
-#         current_data = job.result
-#         ticker = current_data.get("ticker")
-#         company_name = current_data.get("company_name")
-#         intelligence_briefing = current_data.get("intelligence_briefing", {})
-        
-#         llm_report_data = get_llm_analysis(ticker, company_name, intelligence_briefing)
-        
-#         new_result = current_data.copy()
-#         new_result['llm_analysis'] = llm_report_data
-#         job.result = new_result
-        
-#         job.status = "SUCCESS"
-#         db.commit()
-        
-#         print(f"LLM analysis for job {job_id} completed successfully.")
-
-#     except Exception as e:
-#         print(f"Error during LLM analysis for job {job_id}: {e}")
-#         if job:
-#             job.status = "FAILED"
-#             error_data = job.result if job.result else {}
-#             error_data['error'] = f"LLM analysis failed: {str(e)}"
-#             job.result = error_data
-#             db.commit()
-#     finally:
-#         db.close()
-
-
-
-
-
-
-
-
-
-
-# from celery_worker import celery
-# from core.database import SessionLocal
-# from models.analysis_job import AnalysisJob
-# from tools.analyst_tools import get_llm_analysis
-# from uuid import UUID
-
-# @celery.task
-# def run_llm_analysis(job_id: str):
-#     with SessionLocal() as db:
-#         job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first()
-#         if not job or not job.result:
-#             print(f"Job {job_id} not found or has no data for analyst.")
-#             return
-
-#         try:
-#             job.status = "ANALYZING"
-#             db.commit()
-
-#             current_data = job.result
-#             ticker = current_data.get("ticker")
-#             company_name = current_data.get("company_name")
-#             intelligence_briefing = current_data.get("intelligence_briefing", {})
-            
-#             llm_report_data = get_llm_analysis(ticker, company_name, intelligence_briefing)
-            
-#             new_result = dict(current_data)
-#             new_result['llm_analysis'] = llm_report_data
-#             job.result = new_result
-            
-#             job.status = "SUCCESS"
-#             db.commit()
-            
-#             print(f"LLM analysis for job {job_id} completed successfully.")
-#             return "LLM analysis successful."
-#         except Exception as e:
-#             print(f"Error during LLM analysis for job {job_id}: {e}")
-#             job.status = "FAILED"
-#             error_data = job.result if job.result else {}
-#             error_data['error'] = f"LLM analysis failed: {str(e)}"
-#             job.result = error_data
-#             db.commit()
-#             return f"LLM analysis failed: {e}"
-
-
-
-
-
-
-
-
-
-
-
 from celery_worker import celery
 from tools.analyst_tools import get_llm_analysis
 

backend/tasks/data_tasks.py

@@ -1,93 +1,3 @@
-# from celery_worker import celery
-# from core.database import SessionLocal
-# from models.analysis_job import AnalysisJob
-# from tools.data_tools import get_stock_data
-# from uuid import UUID
-
-# @celery.task
-# def run_data_analysis(job_id: str, ticker: str):
-#     db = SessionLocal()
-#     job = None
-#     final_result = ""
-#     try:
-#         job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first()
-#         if not job:
-#             raise ValueError(f"Job {job_id} not found in database.")
-
-#         print(f"Status - DATA_FETCHING for job {job_id}...")
-#         job.status = "DATA_FETCHING"
-#         db.commit()
-
-#         data = get_stock_data(ticker)
-        
-#         if "error" in data:
-#             raise ValueError(data["error"])
-
-#         job.result = data
-#         db.commit()
-#         print(f"Data analysis for job {job_id} completed successfully.")
-        
-#         final_result = str(job.result)
-
-#     except Exception as e:
-#         print(f"Error during data analysis for job {job_id}: {e}")
-#         if job:
-#             job.status = "FAILED"
-#             job.result = {"error": f"Data analysis failed: {str(e)}"}
-#             db.commit()
-#         final_result = f"Error: {e}"
-#     finally:
-#         db.close()
-
-#     return final_result
-
-
-
-
-
-
-
-# from celery_worker import celery
-# from core.database import SessionLocal
-# from models.analysis_job import AnalysisJob
-# from tools.data_tools import get_stock_data
-# from uuid import UUID
-
-# @celery.task
-# def run_data_analysis(job_id: str, ticker: str):
-#     with SessionLocal() as db:
-#         job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first()
-#         if not job:
-#             print(f"Job {job_id} not found.")
-#             return
-
-#         try:
-#             job.status = "DATA_FETCHING"
-#             db.commit()
-
-#             data = get_stock_data(ticker)
-#             if "error" in data:
-#                 raise ValueError(data["error"])
-
-#             job.result = data
-#             db.commit()
-#             print(f"Data analysis for job {job_id} completed successfully.")
-#             return "Data fetching successful."
-#         except Exception as e:
-#             print(f"Error during data analysis for job {job_id}: {e}")
-#             job.status = "FAILED"
-#             job.result = {"error": f"Data analysis failed: {str(e)}"}
-#             db.commit()
-#             return f"Data fetching failed: {e}"
-
-
-
-
-
-
-
-
-
 from celery_worker import celery
 from tools.data_tools import get_stock_data
 

backend/tasks/main_task.py

@@ -1,113 +1,3 @@
-# from celery_worker import celery
-# from core.database import SessionLocal
-# from models.analysis_job import AnalysisJob
-# from tools.data_tools import get_stock_data
-# from tools.news_tools import get_combined_news_and_sentiment
-# from tools.analyst_tools import get_llm_analysis
-# from uuid import UUID
-# import json
-
-# @celery.task
-# def run_full_analysis(job_id: str, ticker: str):
-#     print(f"\n--- [START] Full Analysis for Job ID: {job_id} ---")
-    
-#     # --- Stage 1: Data Fetching ---
-#     try:
-#         with SessionLocal() as db:
-#             job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first()
-#             if not job: raise ValueError("Job not found")
-#             job.status = "DATA_FETCHING"
-#             db.commit()
-#         print("[LOG] STATUS UPDATE: DATA_FETCHING")
-        
-#         data_result = get_stock_data(ticker)
-#         if "error" in data_result: raise ValueError(data_result['error'])
-#         company_name = data_result.get("company_name", ticker)
-        
-#         with SessionLocal() as db:
-#             job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first()
-#             job.result = data_result
-#             db.commit()
-#             db.refresh(job) # Force reload from DB
-#             print(f"[LOG] DB SAVE 1 (Data): Result keys are now: {list(job.result.keys())}")
-
-#     except Exception as e:
-#         print(f"!!! [FAILURE] Stage 1 (Data): {e}")
-#         # ... error handling ...
-#         return
-
-#     # --- Stage 2: Intelligence Gathering ---
-#     try:
-#         with SessionLocal() as db:
-#             job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first()
-#             job.status = "INTELLIGENCE_GATHERING"
-#             db.commit()
-#         print("[LOG] STATUS UPDATE: INTELLIGENCE_GATHERING")
-
-#         intelligence_result = get_combined_news_and_sentiment(ticker, company_name)
-        
-#         with SessionLocal() as db:
-#             job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first()
-#             current_result = dict(job.result)
-#             current_result['intelligence_briefing'] = intelligence_result
-#             job.result = current_result
-#             db.commit()
-#             db.refresh(job) # Force reload
-#             print(f"[LOG] DB SAVE 2 (Intelligence): Result keys are now: {list(job.result.keys())}")
-#     except Exception as e:
-#         print(f"!!! [FAILURE] Stage 2 (Intelligence): {e}")
-#         # ... error handling ...
-#         return
-
-#     # --- Stage 3: LLM Analysis ---
-#     try:
-#         with SessionLocal() as db:
-#             job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first()
-#             job.status = "ANALYZING"
-#             db.commit()
-#         print("[LOG] STATUS UPDATE: ANALYZING")
-
-#         with SessionLocal() as db:
-#             job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first()
-#             data_for_llm = job.result
-        
-#         llm_result = get_llm_analysis(ticker, company_name, data_for_llm.get("intelligence_briefing", {}))
-#         if "error" in llm_result: raise ValueError(llm_result['error'])
-        
-#         # --- Final Assembly and Save ---
-#         print("[LOG] Finalizing results...")
-#         with SessionLocal() as db:
-#             job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first()
-#             final_result_data = dict(job.result)
-#             final_result_data['llm_analysis'] = llm_result
-#             job.result = final_result_data
-#             job.status = "SUCCESS"
-#             db.commit()
-#             db.refresh(job)
-#             print(f"[LOG] DB SAVE 3 (Final): Result keys are now: {list(job.result.keys())}")
-        
-#         print(f"--- [SUCCESS] Full analysis for {job_id} complete. ---")
-
-#     except Exception as e:
-#         print(f"!!! [FAILURE] Stage 3 (LLM): {e}")
-#         with SessionLocal() as db:
-#             job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first()
-#             if job:
-#                 job.status = "FAILED"
-#                 error_data = job.result if job.result else {}
-#                 error_data['error'] = str(e)
-#                 job.result = error_data
-#                 db.commit()
-
-
-
-
-
-
-
-
-
-
 from celery_worker import celery
 from core.database import SessionLocal
 from models.analysis_job import AnalysisJob
@@ -123,9 +13,7 @@ def run_full_analysis(job_id: str, ticker: str):
     The single, main task that orchestrates the entire analysis pipeline.
     """
     print(f"\n--- [START] Full Analysis for Job ID: {job_id} ---")
-    
-    # We will use one job object throughout and update it, committing as we go.
-    # This requires careful session management.
+
     db = SessionLocal()
     job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first()
 

backend/tasks/news_tasks.py

@@ -1,348 +1,7 @@
-# # tasks/news_tasks.py - SIMPLIFIED VERSION THAT ALWAYS WORKS
-
-# from celery_worker import celery
-# from core.database import SessionLocal
-# from models.analysis_job import AnalysisJob
-# from uuid import UUID
-# import logging
-# from datetime import datetime
-# import yfinance as yf
-
-# logger = logging.getLogger(__name__)
-
-# def get_stock_basic_info(ticker: str):
-#     """Get basic stock information to create realistic content"""
-#     try:
-#         stock = yf.Ticker(ticker)
-#         info = stock.info
-#         return {
-#             'name': info.get('longName', ticker.replace('.NS', '')),
-#             'sector': info.get('sector', 'Unknown'),
-#             'industry': info.get('industry', 'Unknown'),
-#             'current_price': info.get('currentPrice', 0),
-#             'previous_close': info.get('previousClose', 0)
-#         }
-#     except Exception as e:
-#         logger.warning(f"Could not get stock info for {ticker}: {e}")
-#         return {
-#             'name': ticker.replace('.NS', ''),
-#             'sector': 'Unknown',
-#             'industry': 'Unknown', 
-#             'current_price': 0,
-#             'previous_close': 0
-#         }
-
-# def create_realistic_articles(ticker: str, company_name: str, stock_info: dict):
-#     """Create realistic articles based on stock information"""
-    
-#     # Calculate price movement for realistic sentiment
-#     current_price = stock_info.get('current_price', 0)
-#     previous_close = stock_info.get('previous_close', 0) 
-    
-#     price_change = 0
-#     if current_price and previous_close:
-#         price_change = ((current_price - previous_close) / previous_close) * 100
-    
-#     # Generate articles based on actual stock performance
-#     articles = []
-    
-#     if price_change > 2:
-#         articles.extend([
-#             {
-#                 "title": f"{company_name} Shares Rally {price_change:.1f}% on Strong Market Sentiment",
-#                 "url": f"https://finance.yahoo.com/quote/{ticker}",
-#                 "source": "Market Analysis",
-#                 "sentiment": "Positive",
-#                 "sentiment_score": 0.8
-#             },
-#             {
-#                 "title": f"Investors Show Confidence in {company_name} as Stock Gains Momentum",
-#                 "url": f"https://www.moneycontrol.com/india/stockpricequote/{ticker}",
-#                 "source": "Financial Express",
-#                 "sentiment": "Positive",
-#                 "sentiment_score": 0.7
-#             }
-#         ])
-#     elif price_change < -2:
-#         articles.extend([
-#             {
-#                 "title": f"{company_name} Stock Declines {abs(price_change):.1f}% Amid Market Volatility",
-#                 "url": f"https://finance.yahoo.com/quote/{ticker}",
-#                 "source": "Market Watch",
-#                 "sentiment": "Negative",
-#                 "sentiment_score": 0.8
-#             },
-#             {
-#                 "title": f"Market Correction Impacts {company_name} Share Price",
-#                 "url": f"https://www.moneycontrol.com/india/stockpricequote/{ticker}",
-#                 "source": "Economic Times",
-#                 "sentiment": "Negative", 
-#                 "sentiment_score": 0.6
-#             }
-#         ])
-#     else:
-#         articles.extend([
-#             {
-#                 "title": f"{company_name} Stock Shows Steady Performance in Current Market",
-#                 "url": f"https://finance.yahoo.com/quote/{ticker}",
-#                 "source": "Yahoo Finance",
-#                 "sentiment": "Neutral",
-#                 "sentiment_score": 0.5
-#             },
-#             {
-#                 "title": f"Technical Analysis: {company_name} Maintains Stable Trading Range", 
-#                 "url": f"https://www.moneycontrol.com/india/stockpricequote/{ticker}",
-#                 "source": "Market Analysis",
-#                 "sentiment": "Neutral",
-#                 "sentiment_score": 0.5
-#             }
-#         ])
-    
-#     # Add sector-specific articles
-#     sector = stock_info.get('sector', 'Unknown')
-#     if sector != 'Unknown':
-#         articles.extend([
-#             {
-#                 "title": f"{sector} Sector Update: Key Players Including {company_name} in Focus",
-#                 "url": "https://example.com/sector-analysis",
-#                 "source": "Sector Reports",
-#                 "sentiment": "Neutral",
-#                 "sentiment_score": 0.6
-#             },
-#             {
-#                 "title": f"Industry Outlook: {stock_info.get('industry', 'Market')} Trends Affecting {company_name}",
-#                 "url": "https://example.com/industry-outlook", 
-#                 "source": "Industry Analysis",
-#                 "sentiment": "Positive",
-#                 "sentiment_score": 0.6
-#             }
-#         ])
-    
-#     # Add general market articles
-#     articles.extend([
-#         {
-#             "title": f"Quarterly Performance Review: {company_name} Financials and Market Position",
-#             "url": f"https://finance.yahoo.com/quote/{ticker}/financials",
-#             "source": "Financial Reports",
-#             "sentiment": "Neutral",
-#             "sentiment_score": 0.5
-#         },
-#         {
-#             "title": f"Analyst Coverage: Investment Recommendations for {company_name} Stock",
-#             "url": "https://example.com/analyst-coverage",
-#             "source": "Research Reports", 
-#             "sentiment": "Positive",
-#             "sentiment_score": 0.7
-#         },
-#         {
-#             "title": f"Market Sentiment Analysis: Retail vs Institutional Interest in {company_name}",
-#             "url": "https://example.com/market-sentiment",
-#             "source": "Market Research",
-#             "sentiment": "Neutral",
-#             "sentiment_score": 0.5
-#         }
-#     ])
-    
-#     return articles[:8]  # Return top 8 articles
-
-# def try_real_news_sources(ticker: str, company_name: str):
-#     """Attempt to get real news, but don't fail if it doesn't work"""
-#     real_articles = []
-    
-#     try:
-#         # Try Yahoo Finance news (most reliable)
-#         logger.info(f"Attempting to fetch real Yahoo Finance news for {ticker}")
-#         stock = yf.Ticker(ticker)
-#         news = stock.news
-        
-#         if news:
-#             logger.info(f"Found {len(news)} Yahoo Finance articles")
-#             for article in news[:5]:  # Take first 5
-#                 if article.get('title'):
-#                     # Simple sentiment analysis
-#                     title_lower = article['title'].lower()
-#                     if any(word in title_lower for word in ['gain', 'rise', 'growth', 'profit', 'strong']):
-#                         sentiment = 'Positive'
-#                         score = 0.7
-#                     elif any(word in title_lower for word in ['fall', 'decline', 'loss', 'weak', 'drop']):
-#                         sentiment = 'Negative'
-#                         score = 0.7
-#                     else:
-#                         sentiment = 'Neutral'
-#                         score = 0.5
-                    
-#                     real_articles.append({
-#                         "title": article['title'].strip(),
-#                         "url": article.get('link', ''),
-#                         "source": article.get('publisher', 'Yahoo Finance'),
-#                         "sentiment": sentiment,
-#                         "sentiment_score": score,
-#                         "is_real": True
-#                     })
-        
-#         logger.info(f"Successfully retrieved {len(real_articles)} real articles")
-        
-#     except Exception as e:
-#         logger.warning(f"Could not fetch real news: {e}")
-    
-#     return real_articles
-
-# @celery.task
-# def run_intelligence_analysis(job_id: str):
-#     """Simplified intelligence analysis that always provides results"""
-#     db = SessionLocal()
-#     job = None
-    
-#     try:
-#         logger.info(f"Starting intelligence analysis for job {job_id}")
-        
-#         # Get job
-#         job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first()
-#         if not job or not job.result:
-#             raise ValueError(f"Job {job_id} not found or has no initial data.")
-        
-#         job.status = "INTELLIGENCE_GATHERING"
-#         db.commit()
-        
-#         current_data = job.result
-#         ticker = current_data.get("ticker")
-#         company_name = current_data.get("company_name", ticker.replace('.NS', ''))
-        
-#         logger.info(f"Analyzing {company_name} ({ticker})")
-        
-#         # Get basic stock information
-#         stock_info = get_stock_basic_info(ticker)
-#         logger.info(f"Stock info: {stock_info['name']} - {stock_info['sector']}")
-        
-#         # Try to get real news first
-#         real_articles = try_real_news_sources(ticker, company_name)
-        
-#         # Create realistic articles
-#         realistic_articles = create_realistic_articles(ticker, company_name, stock_info)
-        
-#         # Combine real and realistic articles
-#         all_articles = real_articles + realistic_articles
-        
-#         # Remove duplicates and limit to 10 articles
-#         seen_titles = set()
-#         unique_articles = []
-#         for article in all_articles:
-#             if article['title'] not in seen_titles:
-#                 seen_titles.add(article['title'])
-#                 unique_articles.append(article)
-        
-#         final_articles = unique_articles[:10]
-        
-#         # Count sentiments
-#         sentiment_counts = {'Positive': 0, 'Negative': 0, 'Neutral': 0}
-#         for article in final_articles:
-#             sentiment_counts[article['sentiment']] += 1
-        
-#         # Create intelligence briefing
-#         intelligence_briefing = {
-#             "articles": final_articles,
-#             "sentiment_summary": {
-#                 "total_items": len(final_articles),
-#                 "positive": sentiment_counts['Positive'],
-#                 "negative": sentiment_counts['Negative'],
-#                 "neutral": sentiment_counts['Neutral'],
-#                 "real_articles": len(real_articles),
-#                 "generated_articles": len(realistic_articles),
-#                 "analysis_timestamp": datetime.now().isoformat()
-#             }
-#         }
-        
-#         # Update job result
-#         new_result = current_data.copy()
-#         new_result['intelligence_briefing'] = intelligence_briefing
-#         job.result = new_result
-#         job.status = "INTELLIGENCE_COMPLETE"
-        
-#         db.commit()
-        
-#         logger.info(f"Intelligence analysis completed successfully:")
-#         logger.info(f"- Total articles: {len(final_articles)}")
-#         logger.info(f"- Real articles: {len(real_articles)}")
-#         logger.info(f"- Generated articles: {len(realistic_articles)}")
-#         logger.info(f"- Sentiment: {sentiment_counts}")
-        
-#         return str(job.result)
-        
-#     except Exception as e:
-#         logger.error(f"Intelligence analysis failed for job {job_id}: {e}")
-        
-#         if job:
-#             job.status = "FAILED"
-#             error_data = job.result if job.result else {}
-#             error_data['error'] = f"Intelligence analysis failed: {str(e)}"
-#             job.result = error_data
-#             db.commit()
-        
-#         return f"Error: {e}"
-        
-#     finally:
-#         db.close()
-
-
-
-
-
-
-
-
-
-# from celery_worker import celery
-# from core.database import SessionLocal
-# from models.analysis_job import AnalysisJob
-# from tools.news_tools import get_combined_news_and_sentiment
-# from uuid import UUID
-
-# @celery.task
-# def run_intelligence_analysis(job_id: str):
-#     with SessionLocal() as db:
-#         job = db.query(AnalysisJob).filter(AnalysisJob.id == UUID(job_id)).first()
-#         if not job or not job.result:
-#             print(f"Job {job_id} not found or has no data for intelligence.")
-#             return
-        
-#         try:
-#             job.status = "INTELLIGENCE_GATHERING"
-#             db.commit()
-
-#             current_data = job.result
-#             ticker = current_data.get("ticker")
-#             company_name = current_data.get("company_name")
-            
-#             intelligence_briefing = get_combined_news_and_sentiment(ticker, company_name)
-            
-#             new_result = dict(current_data)
-#             new_result['intelligence_briefing'] = intelligence_briefing
-#             job.result = new_result
-            
-#             db.commit()
-#             print(f"Intelligence analysis for job {job_id} completed successfully.")
-#             return "Intelligence gathering successful."
-#         except Exception as e:
-#             print(f"Error during intelligence analysis for job {job_id}: {e}")
-#             job.status = "FAILED"
-#             error_data = job.result if job.result else {}
-#             error_data['error'] = f"Intelligence analysis failed: {str(e)}"
-#             job.result = error_data
-#             db.commit()
-#             return f"Intelligence gathering failed: {e}"
-
-
-
-
-
-
-
-
 from celery_worker import celery
 from tools.news_tools import get_combined_news_and_sentiment
 
 @celery.task
 def get_intelligence_task(ticker: str, company_name: str):
     print(f"Executing get_intelligence_task for {company_name}...")
-    # This task now depends on the company_name from the first task's result
     return get_combined_news_and_sentiment(ticker, company_name)

backend/tmp.py

@@ -1,8 +1,6 @@
-# test_news.py - Run this to test the news functionality
 from tools.news_tools import get_combined_news_and_sentiment_debug
 
 def test_news():
-    # Test with a popular Indian stock
     ticker = "RELIANCE.NS"
     company_name = "Reliance Industries"
     

backend/tools/prediction_tools.py

@@ -11,14 +11,10 @@ def generate_forecast(ticker: str) -> Dict[str, Any]:
     if stock_data.empty:
         return {"error": f"Could not download historical data for {ticker}."}
 
-    # --- THE FINAL, MOST ROBUST FIX FOR THE DATAFRAME ---
-    # 1. Create a new DataFrame with only the columns we need.
     df_prophet = stock_data[['Close']].copy()
-    # 2. Reset the index to turn the 'Date' index into a column.
     df_prophet.reset_index(inplace=True)
     # 3. Rename the columns to what Prophet expects.
     df_prophet.columns = ['ds', 'y']
-    # --- END OF FIX ---
 
     model = Prophet(
         daily_seasonality=False,

docker-compose.yml

@@ -1,5 +1,4 @@
 services:
-  # --- Application Services ---
   redis:
     image: redis:7-alpine
     ports:
@@ -53,53 +52,3 @@ services:
 
 
 
-
-
-# services:
-#   redis:
-#     image: redis:7-alpine
-#     ports:
-#       - "6379:6379"
-#     restart: always
-
-#   backend:
-#     build:
-#       context: .
-#       dockerfile: ./backend/Dockerfile
-#     ports:
-#       - "8000:8000"
-#     volumes:
-#       - ./backend:/code/app
-#     env_file:
-#       - .env
-#     command: python -m uvicorn main:app --host 0.0.0.0 --port 8000 --reload
-#     restart: always
-#     depends_on:
-#       - redis
-
-#   worker:
-#     build:
-#       context: .
-#       dockerfile: ./backend/Dockerfile
-#     volumes:
-#       - ./backend:/code/app
-#     env_file:
-#       - .env
-#     command: python -m celery -A celery_worker.celery worker --loglevel=info
-#     restart: always
-#     depends_on:
-#       - redis
-#       - backend
-
-#   frontend:
-#     build:
-#       context: .
-#       dockerfile: ./frontend/Dockerfile
-#     ports:
-#       - "5173:5173"
-#     volumes:
-#       - ./frontend:/app
-#       - /app/node_modules
-#     restart: always
-#     depends_on:
-#       - backend

frontend/src/App.jsx

@@ -1,125 +1,3 @@
-// import React, { useState, useEffect } from 'react';
-// import Header from './components/Header';
-// import JobForm from './components/JobForm';
-// import JobStatusCard from './components/JobStatusCard';
-// import ResultsDisplay from './components/ResultsDisplay';
-// import LoadingSkeleton from './components/LoadingSkeleton';
-// import HistoryPanel from './components/HistoryPanel';
-// import { createJob, getJob } from './services/api';
-// import { XCircle } from 'lucide-react'; 
-
-// function App() {
-//   const [job, setJob] = useState(null);
-//   const [isLoading, setIsLoading] = useState(false); 
-//   const [isPolling, setIsPolling] = useState(false); 
-//   const [error, setError] = useState(null);
-
-//   const handleAnalysisRequest = async (ticker) => {
-//     setIsLoading(true);
-//     setIsPolling(true);
-//     setError(null);
-//     setJob(null);
-//     try {
-//       const response = await createJob(ticker);
-//       setJob(response.data);
-//     } catch (err) {
-//       setError('Failed to create job. Please check the API server and try again.');
-//       setIsLoading(false);
-//       setIsPolling(false);
-//     }
-//   };
-
-//   const handleSelectHistoryJob = (historyJob) => {
-//     setIsLoading(false);
-//     setIsPolling(false);
-//     setError(null);
-//     setJob(historyJob);
-//   }
-
-//   useEffect(() => {
-//     if (!job?.id || !isPolling) return;
-
-//     if (job.status !== 'PENDING') {
-//       setIsLoading(false);
-//     }
-
-//     const intervalId = setInterval(async () => {
-//       try {
-//         const response = await getJob(job.id);
-//         const updatedJob = response.data;
-//         setJob(updatedJob);
-
-//         if (updatedJob.status === 'SUCCESS' || updatedJob.status === 'FAILED') {
-//           clearInterval(intervalId);
-//           setIsPolling(false);
-//         }
-//       } catch (err) {
-//         setError('Failed to poll job status.');
-//         clearInterval(intervalId);
-//         setIsPolling(false);
-//       }
-//     }, 3000);
-
-//     return () => clearInterval(intervalId);
-//   }, [job, isPolling]);
-
-//   return (
-//     <div className="min-h-screen bg-gray-900 text-white font-sans">
-//       <Header />
-//       <HistoryPanel onSelectJob={handleSelectHistoryJob} />
-      
-//       <main className="container mx-auto p-4 md:p-8">
-//         <div className="max-w-4xl mx-auto">
-//           <p className="text-lg text-gray-400 mb-8 text-center">
-//             Enter an Indian stock ticker to receive a comprehensive, AI-powered analysis.
-//           </p>
-          
-//           <JobForm onAnalyze={handleAnalysisRequest} isLoading={isLoading || isPolling} />
-          
-//           {error && <div className="my-6 p-4 bg-red-900/50 rounded-lg text-red-300 text-center">{error}</div>}
-          
-//           {isLoading && !job && <LoadingSkeleton />}
-          
-//           {job && !isLoading && <JobStatusCard job={job} />}
-
-//           {job?.status === 'SUCCESS' && job.result && (
-//             <ResultsDisplay result={job.result} />
-//           )}
-
-//           {job?.status === 'FAILED' && job.result?.error && (
-//              <div className="mt-8 p-6 bg-gray-800/30 border border-red-500/30 rounded-lg text-center animate-fade-in">
-//                <XCircle className="w-16 h-16 text-red-400 mx-auto mb-4" />
-//                <h2 className="text-2xl font-bold text-red-300 mb-2">Analysis Failed</h2>
-//                <p className="text-gray-400 max-w-lg mx-auto">
-//                  We couldn't complete the analysis for <strong className="text-white">{job.ticker}</strong>.
-//                  This usually means the stock symbol is incorrect or not listed.
-//                </p>
-//                <p className="text-xs text-gray-500 mt-4">Please double-check the ticker (e.g., RELIANCE.NS) and try again.</p>
-               
-//                <details className="mt-6 text-left w-full max-w-lg mx-auto">
-//                  <summary className="cursor-pointer text-xs text-gray-500 hover:text-gray-400 focus:outline-none">Show technical details</summary>
-//                  <pre className="mt-2 bg-gray-900 p-4 rounded-md text-gray-400 text-xs whitespace-pre-wrap font-mono">
-//                    {job.result.error}
-//                  </pre>
-//                </details>
-//              </div>
-//           )}
-//         </div>
-//       </main>
-//     </div>
-//   );
-// }
-
-// export default App;
-
-
-
-
-
-
-
-
-
 import React, { useState, useEffect } from 'react';
 import Header from './components/Header';
 import JobForm from './components/JobForm';

frontend/src/components/Header.jsx

@@ -1,5 +1,5 @@
 import React from 'react';
-import { Bot } from 'lucide-react'; // Using a cool icon
+import { Bot } from 'lucide-react'; 
 
 function Header() {
   return (

frontend/src/components/HistoricalChart.jsx

@@ -2,7 +2,6 @@ import React, { useState, useEffect } from 'react';
 import { LineChart, Line, XAxis, YAxis, CartesianGrid, Tooltip, ResponsiveContainer } from 'recharts';
 import axios from 'axios';
 
-// A more reliable public CORS proxy
 const PROXY_URL = 'https://api.allorigins.win/raw?url=';
 
 const fetchHistoricalData = async (ticker) => {
@@ -15,7 +14,6 @@ const fetchHistoricalData = async (ticker) => {
         const timestamps = data.timestamp;
         const prices = data.indicators.quote[0].close;
 
-        // Filter out any null price points which can crash the chart
         return timestamps
             .map((ts, i) => ({
                 date: new Date(ts * 1000).toLocaleDateString('en-IN', {day: 'numeric', month: 'short'}),

frontend/src/components/HistoryPanel.jsx

@@ -11,14 +11,12 @@ function HistoryPanel({ onSelectJob }) {
     setIsLoading(true);
     getJobsHistory()
       .then(response => {
-        // Filter for only completed jobs to make the list cleaner
         setHistory(response.data.filter(job => job.status === 'SUCCESS' || job.status === 'FAILED'));
       })
       .catch(error => console.error("Failed to fetch history:", error))
       .finally(() => setIsLoading(false));
   };
 
-  // When the panel opens, fetch the history
   const togglePanel = () => {
     const newIsOpen = !isOpen;
     setIsOpen(newIsOpen);
@@ -42,7 +40,6 @@ function HistoryPanel({ onSelectJob }) {
         <History className="w-8 h-8" />
       </button>
 
-      {/* Overlay to close panel when clicking outside */}
       {isOpen && <div onClick={() => setIsOpen(false)} className="fixed inset-0 bg-black/50 z-30 transition-opacity"></div>}
 
       <div className={`fixed top-0 right-0 h-full bg-gray-900 border-l border-gray-700 shadow-2xl z-40 transition-transform duration-500 ease-in-out ${isOpen ? 'translate-x-0' : 'translate-x-full'} w-full md:w-96`}>

frontend/src/components/JobForm.jsx

@@ -1,14 +1,13 @@
 import React, { useState } from 'react';
 import { Search, LoaderCircle } from 'lucide-react';
 
-// The component now receives 'onAnalyze' and 'isLoading' as props
 function JobForm({ onAnalyze, isLoading }) {
   const [ticker, setTicker] = useState('');
 
   const handleSubmit = (e) => {
     e.preventDefault();
     if (!ticker.trim() || isLoading) return;
-    onAnalyze(ticker); // Call the function passed down from App.jsx
+    onAnalyze(ticker); 
   };
 
   return (

frontend/src/components/JobStatusCard.jsx

@@ -3,7 +3,6 @@ import { LoaderCircle, CheckCircle2, XCircle, FileClock, Database, Search, Bot }
 
 function JobStatusCard({ job }) {
   const getStatusInfo = (status) => {
-    // This map now perfectly matches the statuses set in your main_task.py
     const statusMap = {
       'PENDING': {
         icon: <FileClock className="w-8 h-8 text-yellow-400" />,

frontend/src/components/LoadingSkeleton.jsx

@@ -1,6 +1,5 @@
 import React from 'react';
 
-// A simple helper for the pulsing effect
 const SkeletonBlock = ({ className }) => (
   <div className={`bg-gray-700 rounded-md animate-pulse ${className}`} />
 );

frontend/src/services/api.js

@@ -1,28 +1,3 @@
-// import axios from 'axios';
-
-// const apiClient = axios.create({
-//   baseURL: 'http://localhost:8000', 
-//   headers: {
-//     'Content-Type': 'application/json',
-//   },
-// });
-
-// export const createJob = (ticker) => {
-//   return apiClient.post('/jobs', { ticker });
-// };
-
-// export const getJob = (jobId) => {
-//   return apiClient.get(`/jobs/${jobId}`);
-// };
-
-
-// export const getJobsHistory = () => {
-//   return apiClient.get('/jobs');
-// };
-
-
-
-
 import axios from 'axios';
 
 const API_URL = import.meta.env.VITE_API_URL || 'http://localhost:8000';

tmp_down.py

@@ -1,6 +1,5 @@
 from transformers import pipeline
 
 print("Downloading NEW model 'ProsusAI/finbert'...")
-# Using the pipeline API is the easiest way to download all necessary files
 classifier = pipeline('text-classification', model='ProsusAI/finbert')
 print("Download complete!")