Update app.py

app.py

@@ -4,28 +4,26 @@ df = pd.read_csv('./Automobile_data.csv')
 df = df.drop(columns = ['normalized-losses','symboling'], axis = 1)
 
 context_data = []
-for i in range(len(df)):  # Loop over rows
-    context = ""
-    for j in range(10):  # Loop over the first 8 columns
-        context += df.columns[j]  # Add column name
-        context += ": "
-        context += str(df.iloc[i][j])  # Convert value to string
-        context += " "
-    context_data.append(context)
+for i in range(len(df)):
+  context = ""
+  for j in range(3):
+    context += df.columns[j]
+    context += ": "
+    context += df.iloc[i][j]
+    context += " "
+  context_data.append(context)
 
-from langchain_groq import ChatGroq
-llm = ChatGroq(model ="llama-3.1-70b-versatile",api_key = "beza")
 
-qa_pair = []
-for i in range(len(df)):
-  Question = "Given the type of  "+df['make'][i]+ " "+ df['body-style'][i][:-1]+", "" what is the price?"
-  Answer   = df['price'][i]
-  input = f"Instruction:\n{Question}\n\nResponse:\n{Answer}"
-  qa_pair.append(input)
+import os
+
+# Get the secret key from the environment
+groq_key = os.environ.get('groq_api_keys')
+
+## LLM used for RAG
+from langchain_groq import ChatGroq
 
-# from langchain_groq import ChatGroq
+llm = ChatGroq(model="llama-3.1-70b-versatile",api_key=groq_key)
 
-# llm = ChatGroq(model="llama-3.1-70b-versatile",api_key= "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")
 ## Embedding model!
 from langchain_huggingface import HuggingFaceEmbeddings
 embed_model = HuggingFaceEmbeddings(model_name="mixedbread-ai/mxbai-embed-large-v1")
@@ -34,24 +32,19 @@ embed_model = HuggingFaceEmbeddings(model_name="mixedbread-ai/mxbai-embed-large-
 from langchain_chroma import Chroma
 
 vectorstore = Chroma(
-    collection_name="car_dataset_store",
+    collection_name="medical_dataset_store",
     embedding_function=embed_model,
     persist_directory="./",
 )
 
-vectorstore.get().keys()
 # add data to vector nstore
 vectorstore.add_texts(context_data)
 
-query = "What is make, number of doors and fuel type?"
-docs = vectorstore.similarity_search(query)
-print(docs[0].page_content)
-
 retriever = vectorstore.as_retriever()
 
 from langchain_core.prompts import PromptTemplate
 
-template = ("""You are a car expert.
+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.
@@ -74,11 +67,6 @@ rag_chain = (
     | StrOutputParser()
 )
 
-from IPython.display import display, Markdown
-
-response = rag_chain.invoke("What is Capital of Rwanda?")
-Markdown(response)
-
 import gradio as gr
 
 def rag_memory_stream(text):
@@ -87,17 +75,21 @@ def rag_memory_stream(text):
         partial_text += new_text
         yield partial_text
 
+examples = ['I feel dizzy', 'what is the possible sickness for fatigue']
+
+
+
 
-title = "Real-time AI App with Groq API and LangChain to Answer car questions"
+title = "Real-time AI App with Groq API and LangChain to Answer medical questions"
 demo = gr.Interface(
     title=title,
     fn=rag_memory_stream,
     inputs="text",
     outputs="text",
+    examples=examples,
     allow_flagging="never",
 )
 
-demo.launch(share=True)
 
 if __name__ == "__main__":
     demo.launch()