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main.py

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+import os
+import streamlit as st
+from langchain.llms import HuggingFaceHub
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+
+from models import llms
+
+class UserInterface():
+
+    def __init__(self, ):
+        st.warning("Warning: Some models may not work and some models may require GPU to run")
+        st.text("An Open Source Chat Application")
+        st.header("Open LLMs")
+
+        self.API_KEY = st.sidebar.text_input(
+            'API Key',
+            type='password',
+            help="Type in your HuggingFace API key to use this app"
+        )
+
+        models_name = (
+            "HuggingFaceH4/zephyr-7b-beta",
+            "Open-Orca/Mistral-7B-OpenOrca",
+        )
+        self.models = st.sidebar.selectbox(
+            label="Choose your models",
+            options=models_name,
+            help="Choose your model",
+        )
+
+        self.temperature = st.sidebar.slider(
+            label='Temperature',
+            min_value=0.1,
+            max_value=1.0,
+            step=0.1,
+            value=0.5,
+            help="Set the temperature to get accurate or random result"
+        )
+
+        self.max_token_length = st.sidebar.slider(
+            label="Token Length",
+            min_value=32,
+            max_value=2048,
+            step=16,
+            value=64,
+            help="Set max tokens to generate maximum amount of text output"
+        )
+
+
+        self.model_kwargs = {
+            "temperature": self.temperature,
+            "max_length": self.max_token_length
+        }
+
+        os.environ['HUGGINGFACEHUB_API_TOKEN'] = self.API_KEY
+
+    
+    def form_data(self):
+
+        try:
+            if not self.API_KEY.startswith('hf_'):
+                st.warning('Please enter your API key!', icon='⚠')
+                text_input_visibility = True
+            
+            
+            st.subheader("Context")
+            context = st.chat_input(disabled=text_input_visibility)
+            st.subheader("Question")
+            question = st.chat_input(disabled=text_input_visibility)
+
+
+            template = """
+            Answer the question based on the context, if you don't know then output "Out of Context"
+            Context: {context}
+            Question: {question}
+
+            Answer: 
+            """
+            prompt = PromptTemplate(
+                template=template,
+                input_variables=[
+                    'question',
+                    'context'
+                ]
+            )
+            llm = HuggingFaceHub(
+                repo_id = self.model_name,
+                model_kwargs = self.model_kwargs
+            )
+
+            llm_chain = LLMChain(
+                prompt=prompt,
+                llm=llm,
+            )
+
+            result = llm_chain.run({
+                "question": question,
+                "context": context
+            })
+
+            st.markdown(result)
+        except Exception as e:
+            st.error(e, icon="🚨")
+
+model = UserInterface()
+model.form_data()

requirements.txt

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+langchain
+huggingface_hub
+Streamlit