Create app.py

app.py

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+from langchain.chains.llm import LLMChain
+from langchain.chains.sequential import SequentialChain
+from langchain_groq import ChatGroq
+from langchain.prompts import PromptTemplate
+import streamlit as st
+
+# Streamlit Title
+st.title('AI TRADER')
+
+# Input for trading details
+traders_info = st.text_input('Enter the Trading Details from Market Research and Technical Analysis')
+submit = st.button('SUBMIT')
+
+# LLM Model Initialization
+LLM_model = ChatGroq(
+    temperature=0.6,
+    groq_api_key='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'
+)
+
+# Prompt Templates and Chains
+prompt1 = PromptTemplate(
+    input_variables=['input'],
+    template='Based on {input}, which share price will give the highest returns in future options? Summarize in 30 words.'
+)
+chain1 = LLMChain(llm=LLM_model, prompt=prompt1, output_key='shares')
+
+prompt2 = PromptTemplate(
+    input_variables=['shares'],
+    template='What is the current price of {shares}, and what will be the predicted price after five minutes?'
+)
+chain2 = LLMChain(llm=LLM_model, prompt=prompt2, output_key='price_prediction')
+
+prompt3 = PromptTemplate(
+    input_variables=['shares'],
+    template='Name five shares with positive daily growth trends based on the analysis of {shares}.'
+)
+chain3 = LLMChain(llm=LLM_model, prompt=prompt3, output_key='positive_growth_shares')
+
+# Sequential Chain
+parent_chain = SequentialChain(
+    chains=[chain1, chain2, chain3],
+    input_variables=['input'],
+    output_variables=['shares', 'price_prediction', 'positive_growth_shares']
+)
+
+# Streamlit Logic
+if submit:
+    if traders_info.strip():
+        result = parent_chain({'input': traders_info})
+        st.write('**Suggested Shares:**', result['shares'])
+        st.write('**Price Prediction:**', result['price_prediction'])
+        st.write('**Positive Growth Shares:**', result['positive_growth_shares'])
+    else:
+        st.warning('Please provide trading details to proceed.')
+