Create app.py

app.py

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+import streamlit as st
+from langchain.agents import create_react_agent, AgentExecutor
+from langchain.tools.tavily_search import TavilySearchResults
+from langchain_community.tools import DuckDuckGoSearchRun
+from langchain.memory import ConversationBufferMemory
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from langchain_google_genai import ChatGoogleGenerativeAI
+
+
+# Set page config
+st.set_page_config(page_title="Company Info Agent", layout="wide")
+
+st.title("📈 Business Intelligence Assistant")
+st.markdown(
+    "Ask anything about companies: *stock, revenue, growth, acquisitions, news*, etc."
+)
+
+
+# --- Your custom prompt ---
+prompt = PromptTemplate(
+    input_variables=["input", "agent_scratchpad", "tool_names", "tools"],
+    template="""You are a smart and reliable business assistant.
+
+You help users by answering questions about *any company* (public or private), including their:
+- Stock performance
+- Revenue
+- Funding
+- Acquisitions
+- Market valuation
+- Recent news
+- Business activities
+
+You can search online to gather accurate and current data. You must cite sources (like URLs or page titles) in your final answer.
+
+You have access to the following tools:
+
+{tools}
+
+Use the following format:
+
+Question: the input question you must answer  
+Thought: think step-by-step about what to do  
+Action: the action to take, should be one of [{tool_names}]  
+Action Input: the input to the action  
+Observation: the result of the action  
+
+(Repeat the above Thought/Action/Action Input/Observation format only *2 times*)
+
+Then:
+
+Thought: I now know the final answer  
+Final Answer: provide a detailed answer, *include helpful insights and links to your sources*.  
+If no specific info is available, say so politely and try to offer related context.
+
+---
+
+Begin!
+
+Question: {input}  
+Thought:{agent_scratchpad}
+"""
+)
+
+from re import search
+# search_tool = TavilySearchResults(
+#     tavily_api_key = "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",
+#     depth = 'advanced',
+# )
+search_tool = DuckDuckGoSearchRun()
+
+tools = [search_tool]
+
+# --- Initializing the LLM
+llm = ChatGoogleGenerativeAI(
+    api_key = "AIzaSyBYBRKYkgZm5OYM1XQYWlrz9psaS3t65Cg",
+    model = "gemini-2.0-flash"
+)
+
+# User input
+user_input = st.text_input("Enter your question:")
+
+agent = create_react_agent(llm=llm, tools=tools, prompt=prompt)
+
+# Create executor
+agent_executor = AgentExecutor(
+    agent=agent,
+    tools=tools,
+    verbose=True,
+    handle_parsing_errors=True,
+)
+    
+# Run on user input
+if user_input:
+    with st.spinner("🔎 Thinking..."):
+        response = agent_executor.invoke({"input": user_input})
+    st.markdown("### 🧠 Answer:")
+    st.write(response["output"])