Update app.py

app.py

@@ -1,62 +1,113 @@
+import pandas as pd
+
+
+df = pd.read_csv('./medical_data.csv')
+df1=pd.read_csv('./DrugData.csv')
+df11=pd.read_csv('./drugs_side_effects_drugs_com.csv')
+
+context_data = []
+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)
+
+context_data # Initialize the list to store context data
+
+for i in range(len(df1)):  # Iterate through the rows of df1
+    context = ""
+    for j in range(19):  # Iterate through the first 19 columns
+        context += df1.columns[j]  # Add the column name
+        context += ": "
+        context += str(df1.iloc[i][j])  # Convert the value to a string
+        context += " "  # Add a space between entries
+    context_data.append(context)  # Append the generated context to the list
+context_data # Initialize the list to store context data
+
+for i in range(len(df11)):  # Iterate through the rows of df1
+    context = ""
+    for j in range(17):  # Iterate through the first 19 columns
+        context += df11.columns[j]  # Add the column name
+        context += ": "
+        context += str(df11.iloc[i][j])  # Convert the value to a string
+        context += " "  # Add a space between entries
+    context_data.append(context)  # Append the generated context to the list
+
+
+import os
+
+# Get the secret key from the environment
+groq_key = os.environ.get('groq_key')
+
+## LLM used for RAG
+from langchain_groq import ChatGroq
+
+llm = ChatGroq(model="llama-3.1-70b-versatile",api_key=groq_key)
+
+## Embedding model!
+from langchain_huggingface import HuggingFaceEmbeddings
+embed_model = HuggingFaceEmbeddings(model_name="mixedbread-ai/mxbai-embed-large-v1")
+
+# create vector store!
+from langchain_chroma import Chroma
+
+vectorstore = Chroma(
+    collection_name="medical_dataset_store",
+    embedding_function=embed_model,
+    persist_directory="./",
+)
+
+# add data to vector nstore
+vectorstore.add_texts(context_data)
+
+retriever = vectorstore.as_retriever()
+
+from langchain_core.prompts import PromptTemplate
+
+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)
+
+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
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+
+def rag_memory_stream(text):
+    partial_text = ""
+    for new_text in rag_chain.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 medical questions"
+demo = gr.Interface(
+    title=title,
+    fn=rag_memory_stream,
+    inputs="text",
+    outputs="text",
+    examples=examples,
+    allow_flagging="never",
 )