REMOVED keys

src/requirements.txt

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+openpyxl
+nltk
+scikit-learn
+textblob
+feedparser
+openai
+openpyxl
+yfinance
+ollama
+transformers

src/simple_openai_news_to_stock.py

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+# # Latest code 0 use this
+# # https://github.com/openai/openai-cookbook/blob/main/examples/How_to_format_inputs_to_ChatGPT_API.ipynb
+# # https://cookbook.openai.com/
+# import openai
+
+# import requests
+# from textblob import TextBlob
+# import yfinance as yf
+
+# # # Example text for sentiment analysis
+# text = "RBI MPC October After US Federal Reserve reduced rates last month eyes Reserve Bank India RBI A rate cut could signal end rate era fixed deposits The RBI Monetary Policy Committee announcement October provide clarity rate change What best strategy fixed deposits"
+
+# # Sentiment Analysis
+# analysis = TextBlob(text)
+# sentiment = analysis.sentiment
+
+# # Sector Identification
+# sectors = {
+#     'banks': ['SBI.NS', 'HDFC.NS', 'KOTAKBANK.NS', 'AXISBANK.NS',],
+#     'auto': ['MARUTI.NS' 'ASHOKLEY.NS', 'TVSMOTOR.NS'],
+#     'infrastructure': ['LT.NS', 'BEL.NS']
+# }
+
+# # Stock Recommendations based on sentiment
+# if sentiment.polarity > 0:
+#     recommended_stocks = sectors['banks']  # Example
+# else:
+#     recommended_stocks = sectors['auto']  # Example
+
+# # Fetching Stock Data
+# def get_stock_info(tickers):
+#     stock_info = {}
+#     for ticker in tickers:
+#         stock = yf.Ticker(ticker)
+#         stock_info[ticker] = stock.info
+#     return stock_info
+
+# # Example API Call to get stock data
+# stocks_info = get_stock_info(recommended_stocks)
+
+# print(stocks_info)
+
+
+# # openai.api_version = "v1"
+# # response = openai.Completion.create(
+# #     engine="text-davinci-002", 
+# #     prompt="Analyze this text for sentiment: " + text,
+# #     max_tokens=50
+# # )
+# # model = openai.Model("text-davinci-002")
+# # prompt = "Analyze this text for sentiment: " + text
+# # response = model.completion(prompt=prompt, max_tokens=50)
+
+# # sentiment_result = response.choices[0].text
+# # =================================
+# # Function to get stock recommendations based on sentiment
+# def get_stock_recommendations(sentiment_score):
+#     # response = client.completions.create(model="gpt-4-turbo",
+#     #     # engine="text-davinci-002", 
+#     #     prompt="Analyze this text for sentiment: " + text,
+#     #     max_tokens=50
+#     # )
+#     # response = client.chat.completions.create(
+#     #     model="gpt-4o-mini",
+#     #     prompt="Analyze this text for sentiment: " + text,
+#     #     max_tokens=50
+#     # )
+#     response = client.chat.completions.create(
+#         model="gpt-4o-mini",  # e.g. gpt-35-instant
+#         messages=[
+#             {
+#                 "role": "user",
+#                 "content": "Analyze this text for sentiment: " + text,
+#             },
+#         ],
+#     )
+#     recommendations = response.choices[0]['message']['content'].strip()
+#     return recommendations
+
+# # Example usage
+# sentiment_score = 0.7  # Assume some sentiment score here
+# recommendations = get_stock_recommendations(sentiment_score)
+# print("Stock Recommendations:", recommendations)
+
+# ========= Working sanity test with OpenAI prompt engineering ==========
+# from openai import OpenAI
+
+# # Example text for sentiment analysis
+# text = "RBI MPC October After US Federal Reserve reduced rates last month eyes Reserve Bank India RBI A rate cut could signal end rate era fixed deposits The RBI Monetary Policy Committee announcement October provide clarity rate change What best strategy fixed deposits"
+
+
+# stream = client.chat.completions.create(
+#     model="gpt-4o-mini",
+#     messages=[{"role": "user", "content": "Analyze this text for sentiment: " + text}],
+#     stream=True,
+# )
+# for chunk in stream:
+#     if chunk.choices[0].delta.content is not None:
+#         print(chunk.choices[0].delta.content, end="")
+# ===================
+import ollama
+# import os
+# os.environ["CUDA_VISIBLE_DEVICES"] = "0"  # Use the first GPU
+
+# Example function to generate a response from the model
+def generate_response(prompt):
+    response = ollama.chat(model='llama3.1:8b', messages=[{'role': 'user', 'content': prompt}])
+    print (response['message']['content'])
+
+    #  -------------- Working stream mode chat ----------------
+    # stream = ollama.chat(model='llama3.1:8b', messages=[{'role': 'user', 'content': prompt}], stream=True)
+    #     #  (model='llama3.2:3b', messages=[{'role': 'user', 'content': 'Why is the sky blue?'}, stream=True])
+    # for chunk in stream:
+    #     print(chunk['message']['content'], end='', flush=True)
+    # ---------------------
+    do_sentiment = input("---\nDo you want to run sentiment analysis for this news? (Y/N)").upper()
+    if do_sentiment == "Y":
+        prompt = f"Analyze this text for sentiment: " + response['message']['content']
+        sentiment = ollama.chat(model='llama3.1:8b', messages=[{'role': 'user', 'content': prompt}])
+        print("--------------------------")
+        print(f"Sentiment Analysis Result from news: {sentiment['message']['content']}")
+        print("--------------------------")
+        return sentiment
+    else:
+        print("Thank you for using the sentiment analysis tool. Goodbye!")
+
+# text = "RBI MPC October After US Federal Reserve reduced rates last month eyes Reserve Bank India RBI A rate cut could signal end rate era fixed deposits The RBI Monetary Policy Committee announcement October provide clarity rate change What best strategy fixed deposits"
+text = input("Enter your NEWS link: ")
+print("Analysing sentiment of NEWS: " + text)
+# Test the function
+# print(generate_response("Tell me about machine learning."))
+# print(generate_response("Analyze this text for sentiment: " + text))
+print(generate_response("Analyze NEWS from this link" + text))
+# Stocktitan news source - get basic information like What is the current stock price of SOBR Safe (SOBR)?
+# The current stock price of SOBR Safe (SOBR) is $8 as of October 24, 2024.
+
+# What is the market cap of SOBR Safe (SOBR)?
+# The market cap of SOBR Safe (SOBR) is approximately 18.7M.
+
+# What is SOBR Safe, Inc. known for?
+# SOBR Safe specializes in touch-based identity verification and alcohol detection systems.
+
+# Where is SOBR Safe, Inc. headquartered?
+# The company is headquartered in Boulder, Colorado.
+
+# How does SOBR Safe generate revenue?
+# SOBR Safe generates revenue through the sale of cloud-based software solutions, hardware devices, and data reporting services.
+
+# What are the applications of SOBR Safe's technology?
+# The technology has applications in commercial fleet management, school buses, manufacturing facilities, and warehousing.
+
+# What is the focus of SOBR Safe's technology?
+# SOBR Safe focuses on providing statistical and measurable user data with its scalable technology.
+