Update app.py

app.py

@@ -1,22 +1,31 @@
 import streamlit as st
-from transformers import pipeline
+from langchain.llms import HuggingFaceHub
 
-# Load the conversational pipeline
-conversational_pipeline = pipeline("conversational")
+# Function to return the response
+def generate_answer(query):
+    llm = HuggingFaceHub(
+        repo_id="huggingfaceh4/zephyr-7b-alpha",
+        model_kwargs={"temperature": 0.5, "max_length": 64, "max_new_tokens": 512}
+    )
+    prompt = f"""
+        You are helpful doctor assistant that gives patients advices. Please answer the patients in a kind and helpful way.
+        </s>
+        {query}</s>
+    """
+    result = llm.predict(prompt)
+    return result
 
-# Streamlit app header
-st.title("Hugging Face Conversational Model Demo")
+# App UI starts here
+st.set_page_config(page_title="LangChain Demo", page_icon=":robot:")
+st.header("LangChain Demo")
 
-# Input for user message
-user_message = st.text_input("You:", "")
+# Gets User Input
+user_input = st.text_input("You: ", key="input")
 
-if st.button("Send"):
-    # Format the conversation for the conversational pipeline
-    conversation_history = [{"role": "system", "content": "You are a helpful assistant."},
-                           {"role": "user", "content": user_message}]
+submit = st.button("Generate")
 
-    # Get the model's response
-    model_response = conversational_pipeline(conversation_history)[0]['generated_responses'][0]
-
-    # Display the model's response
-    st.text_area("Model:", model_response, height=100)
+# If the button clicked
+if submit:
+    st.subheader("Answer:")
+    response = generate_answer(user_input)
+    st.write(response)