Update app.py

app.py

@@ -1,54 +1,52 @@
 import pandas as pd
+
+context_data = pd.read_csv("drugs_side_effects_drugs_com.csv")
+
 import os
 from langchain_groq import ChatGroq
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain_chroma import Chroma
-from langchain_core.prompts import PromptTemplate
-from langchain_core.output_parsers import StrOutputParser
-from langchain_core.runnables import RunnablePassthrough
-import gradio as gr
 
-# Load data
-context_data = pd.read_csv("drugs_side_effects_drugs_com.csv")
+llm = ChatGroq(model="llama-3.1-70b-versatile",api_key=os.environ.get("GROQ_API_KEY"))
 
-# Initialize LLM
-llm = ChatGroq(
-    model="llama-3.1-70b-versatile",
-    api_key=os.environ.get("GROQ_API_KEY")
-)
+## Embedding model!
+from langchain_huggingface import HuggingFaceEmbeddings
+embed_model = HuggingFaceEmbeddings(model_name="mixedbread-ai/mxbai-embed-large-v1")
 
-# Initialize embedding model
-embed_model = HuggingFaceEmbeddings(
-    model_name="mixedbread-ai/mxbai-embed-large-v1"
-)
+# create vector store!
+from langchain_chroma import Chroma
 
-# Create and populate vector store
 vectorstore = Chroma(
     collection_name="medical_dataset_store",
     embedding_function=embed_model,
     persist_directory="./",
 )
-vectorstore.add_texts(texts=context_data.values.tolist())  # Convert DataFrame to list of texts
+
+# add data to vector nstore
+vectorstore.add_texts(context_data)
+
 retriever = vectorstore.as_retriever()
 
-# Set up RAG prompt template
-template = """You are a medical expert. Use the provided context to answer the question. 
-If you don't know the answer, say so. Explain your answer in detail. 
-Do not discuss the context in your response; just provide the answer directly.
+from langchain_core.prompts import PromptTemplate
 
-Context: {context}
-Question: {question}
-Answer:"""
+template = ("""You are a medical expert.
+    Use the provided context to answer the question.
+    If you don't know the answer, say so. Explain your answer in detail.
+    Do not discuss the context in your response; just provide the answer directly.
+    Context: {context}
+    Question: {question}
+    Answer:""")
 
 rag_prompt = PromptTemplate.from_template(template)
 
-# Create RAG chain
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+
 rag_chain = (
     {"context": retriever, "question": RunnablePassthrough()}
     | rag_prompt
     | llm
     | StrOutputParser()
 )
+import gradio as gr
 
 def rag_memory_stream(message, history):
     partial_text = ""
@@ -56,26 +54,26 @@ def rag_memory_stream(message, history):
         partial_text += new_text
         yield partial_text
 
-# Gradio interface setup
-greetings_message = """Hello! Welcome to MediGuide ChatBot. I'm here to provide you with quick and 
-accurate information on medical drugs. Whether you need details on usage, side effects, etc. feel 
-free to ask. Let's enhance patient care together!"""
+greetingsmessage = """Hello! Welcome to MediGuide ChatBot. I'm here to provide you with quick and accurate information on medical drugs. 
+Whether you need details on usage, side effects , etc feel free to ask. Let's enhance patient care together!"""
+
+initial_history = [("", greetingsmessage)]
+
+title = "MediGuide ChatBot"
 
-initial_history = [("", greetings_message)]
 
-# Create Gradio interface
 demo = gr.Interface(
-    title="MediGuide ChatBot",
+    title=title,
     fn=rag_memory_stream,
     inputs=[
-        gr.Chatbot(value=initial_history, label="Chat History"),
-        gr.Textbox(label="Your Message", placeholder="Type your message here...")
+    gr.Chatbot(value=initial_history, label="Chat History"),"text"
     ],
-    outputs=gr.Chatbot(label="Chat History"),
+    outputs=[gr.Chatbot(label="Chat History"),"text"],
     allow_flagging="never",
-    theme="glass"
+    fill_height=True,
+    theme="glass",
 )
 
+
 if __name__ == "__main__":
-    # Launch with sharing enabled to avoid localhost issues
-    demo.launch(share=True)
+    demo.launch()