Create app.py

app.py

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+import streamlit as st
+import os
+from streamlit_chat import message
+from langchain.prompts import PromptTemplate
+from langchain import LLMChain
+from langchain_community.llms.huggingface_hub import HuggingFaceHub
+
+llm = HuggingFaceHub(repo_id="suriya7/MaxMini-Instruct-248M",
+                     task ='text2text-generation',
+                    huggingfacehub_api_token=os.getenv('HF_TOKEN'),
+                    model_kwargs={
+                    "do_sample":True,
+                    "max_new_tokens":250
+                    })
+
+
+template = """
+Please Answer the Question: 
+previous chat: {previous_history}
+Human:{question}
+chatbot: 
+"""
+
+prompt = PromptTemplate(template=template,input_variables=['question','previous_history'])
+
+llm_chain = LLMChain(
+    llm=llm,
+    prompt=prompt,
+    verbose=True,
+)
+
+previous_response = ""
+def conversational_chat(user_query):
+    previous_response = "".join([f"Human: {i[0]}\nChatbot: {i[1]}" for i in st.session_state['history'] if i is not None])
+    result = llm_chain.predict(
+        question=user_query, 
+        previous_history = previous_response
+    )
+    st.session_state['history'].append((user_query, result))
+    return result
+
+
+st.title('MaxMini')
+st.info("MaxMini-Instruct-248M is a T5 (Text-To-Text Transfer Transformer) model fine-tuned on a variety of tasks. This model is designed to perform a range of instructional tasks, enabling users to generate instructions for various inputs.")
+
+st.session_state['history'] = []
+
+if 'message' not in st.session_state:
+    st.session_state['message'] = ['Hey There! How Can I Assist You']
+
+st.session_state['past'] = []
+
+
+# Create containers for chat history and user input
+response_container = st.container()
+container = st.container()
+
+# User input form
+user_input = st.chat_input("Ask Your Questions 👉..")
+with container:
+    if user_input:
+        output = conversational_chat(user_input)
+        # answer = response_generator(output)
+        st.session_state['past'].append(user_input)
+        st.session_state['message'].append(output)
+        
+        
+# Display chat history
+if st.session_state['message']:
+    with response_container:
+        for i in range(len(st.session_state['message'])):
+            if i != 0:
+                message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="adventurer")
+            message(st.session_state["message"][i], key=str(i), avatar_style="bottts")