Update app.py

app.py

@@ -1,39 +1,43 @@
 import streamlit as st
 from langchain.llms import HuggingFaceHub
-from langchain.chains import LLMChain
-from langchain.prompts import PromptTemplate
 
-# Function to return the response
+#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}
+        repo_id = "huggingfaceh4/zephyr-7b-alpha", 
+        model_kwargs={"temperature": 0.5, "max_length": 64,"max_new_tokens":512}
     )
-
-    template = """Question: {query}
-         
-    Answer: Let's give medical advices in kind way."""
-            
-    prompt = PromptTemplate(template=template, input_variables=["query"])
-    llm_chain = LLMChain(prompt=prompt, llm=llm)
-    result = llm_chain.run(query)
+    prompt = f"""
+            You are a doctor assistant trained to provide medical advice and support. Please respond with empathy and consider the patient's well-being.
+            </s>
+             
+            {query}</s>
+             
+        """
+    result = llm.predict(prompt)
     return result
+    
+
+#App UI starts here 
+st.set_page_config(page_title = "LangChain Demo", page_icon = ":robot:")
+st.header("LangChain Demo")
 
-# App UI starts here
-st.set_page_config(page_title="Doctor Assistant Demo", page_icon=":robot:")
-st.header("Doctor Assistant Demo")
 
-# Gets User Input
+#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 the button clicked
 if submit:
-    st.subheader("Doctor's Response:")
-    st.write(response)
+    st.subheader("Answer: ")
+    st.write(response)
+
+
+