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app.py

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+from huggingface_hub import login
+from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel, tool, Tool, load_tool, InferenceClientModel
+from smolagents.models import ChatMessage
+from transformers import pipeline
+import cohere
+from gradio_client import Client
+from newsapi import NewsApiClient
+import requests
+import gradio as gr
+import os
+from dotenv import load_dotenv
+import json
+from transformers import pipeline
+from mistralai import Mistral
+
+load_dotenv()
+
+newsApiKey = os.getenv('NEWSAPI_KEY')
+#grok_api_key = os.getenv('GROK_API_KEY')
+#HF_TOKEN = os.getenv("HF_TOKEN")
+#login(token=HF_TOKEN)
+COHERE_API_KEY = os.getenv('COHERE_API_KEY')
+
+from groq import Groq
+
+#client = Groq(
+    #api_key=os.environ.get("GROQ_API_KEY"),
+#)
+
+
+
+api_key = os.environ["MISTRAL_API_KEY"]
+model = "mistral-large-latest"
+
+client = Mistral(api_key=api_key)
+
+
+
+#HfApiModel("mistralai/Mistral-7B-v0.1-chat")
+
+def mainFunc(articles, risk_factor):
+    if isinstance(articles, str):
+        articles = json.loads(articles)
+    newsApiKey = os.getenv('NEWSAPI_KEY')
+    if not newsApiKey:
+        raise ValueError("Missing NEWS_API_KEY in environment variables.")
+    
+    print(f"data structure of articles is: {articles}")
+
+    prompt = f"""
+You are an agent that analyzes the risk factors for a company, by using the data from:
+- Documents: {articles['company_info']['documents']}
+- News summaries: {articles['news_data']['articles_summary']}. The user wants to know whether a specific risk factor exists: {risk_factor}. Use the information you are provided to evaluate whether it does. respond in the format of yes/no, and then provide reason/s.
+
+
+"""
+
+
+    chat_completion = client.chat.complete(
+    model=model,
+    messages=[
+        {
+            "role": "system",
+            "content": prompt
+        },
+        {
+            "role": "user",
+            "content": f"{risk_factor}",
+        }
+    ],
+    #and include word 'json' in messages/prompt
+    )
+    print(chat_completion.choices[0].message.content)
+    return chat_completion.choices[0].message.content
+
+    
+    
+    return result
+
+
+#add agents it can hand off to
+
+
+
+
+
+#agent.prompt_templates["system_prompt"] = agent.prompt_templates["system_prompt"] + "\n when asked for most recent articles, return each article with its dict/list values, rather than just the title"
+#agent.run("what are the most recent articles about Microsoft?")
+#print(agent.prompt_templates["system_prompt"])
+
+
+#huggingface-cli login - to set access token in temrainl and save it
+#translation function works well
+
+demo = gr.Interface(
+    fn=mainFunc,
+    inputs=["text", "text"],
+    outputs="text",
+    title="dynamic specific risk",
+    description="finds info about a company"
+)
+
+demo.launch(share=True)