Update app.py

app.py

@@ -1,24 +1,46 @@
 import streamlit as st
-from transformers import pipeline
+from langchain.llms import HuggingFaceHub
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
 
-# Load TinyLlama chatbot pipeline
-pipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v1.0", device_map="auto")
+#Function to return the response 
+def generate_answer(query):
+    llm = HuggingFaceHub(
+        repo_id = "google/flan-t5-xxl", 
+        model_kwargs={"temperature": 0.7, "max_length": 64,"max_new_tokens":512}
+    )
+    
+    template = """Question: {query}
 
-# Streamlit app header
-st.set_page_config(page_title="Chatbot Demo", page_icon="🤖")
-st.header("Chatbot Demo")
+                Answer: Let's think step by step.
+                """
+    
+    PromptTemplate(template=template, input_variables=["query"])
+    llm_chain = LLMChain(prompt=prompt, llm=llm)
+    result = llm_chain.run(query)
+    return result
+    
 
-# Input for user message
-user_message = st.text_input("You:", "")
+#App UI starts here 
+st.set_page_config(page_title = "LangChain Demo", page_icon = ":robot:")
+st.header("LangChain Demo")
+
+
+#Gets User Input 
+def get_text():
+    input_text = st.text_input("You: ", key="input")
+    return input_text
+
+
+user_input = get_text()
+response = generate_answer(user_input)
+
+submit = st.button("Generate")
+
+#If the button clicked
+if submit:
+    st.subheader("Answer: ")
+    st.write(response)
 
-if st.button("Send"):
-    # Use TinyLlama chatbot pipeline to generate a response
-    messages = [{"role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate"},
-                {"role": "user", "content": user_message}]
-    prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-    
-    # Generate response using TinyLlama
-    response = pipe(prompt, max_length=256, temperature=0.7, top_k=50, top_p=0.95)[0]["generated_text"]
 
-    # Display the model's response
-    st.text_area("Model Response:", response, height=100)
+