Update app.py

app.py

@@ -1,64 +1,83 @@
+# # Import embedding model
+from langchain_huggingface import HuggingFaceEmbeddings
+embed_model = HuggingFaceEmbeddings(model_name="mixedbread-ai/mxbai-embed-large-v1")
+from langchain_core.prompts import PromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
 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)",
-        ),
-    ],
+import pandas as pd
+
+from langchain_groq import ChatGroq
+# Create a vector store...
+from langchain_chroma import Chroma
+import os
+
+vectorstore = Chroma(
+    collection_name="medical_dataset_store",
+    embedding_function=embed_model,
+    persist_directory="./",
+)
+vectorstore.get().keys()
+
+# Load the dataset to be used.
+context = pd.read_csv("./drugs_side_effects_drugs_com.csv")
+
+# Because the vector store is empty... Add your context data.
+vectorstore.add_texts(context)
+
+retriever = vectorstore.as_retriever()
+
+
+template = ("""
+You are a medical expert specializing in pharmacology.
+Your task is to use the provided context to answer questions about drug side effects for patients.
+Please follow these guidelines:
+- Provide accurate and detailed answers based on the context.
+- If you don't know the answer, clearly state that you don't know.
+- Do not reference the context directly in your response; just provide the answer.
+- Ensure your answers are clear, concise, and informative.
+Context: {context}
+Question: {question}
+Answer:
+""")
+rag_prompt = PromptTemplate.from_template(template)
+
+# Initialize the model
+llm_model = ChatGroq(model="llama-3.3-70b-versatile", api_key=os.environ.get("GROQ_API_KEY"))
+rag_chain = (
+    {"context": retriever, "question": RunnablePassthrough()}
+    | rag_prompt
+    | llm_model
+    | StrOutputParser()
+)
+
+def rag_memory_stream(message, history):
+    partial_text = ""
+    for new_text in rag_chain.stream(message):
+        partial_text += new_text
+        yield partial_text
+
+examples = [
+    "What is a drug ?", 
+    "What are the side effects of lisinopril?"
+]
+
+description = "Real-Time AI-Powered Medical Assistant: Drug Side Effect Queries Chatbot"
+
+
+title = "AI-Powered Medical Chatbot :) Try me!"
+demo = gr.ChatInterface(fn=rag_memory_stream,
+                        type="messages",
+                        title=title,
+                        description=description,
+                        fill_height=True,
+                        examples=examples,
+                        theme="glass",
 )
 
+# Launch the application and make it sharable
+demo.launch(share=True)
+
 
 if __name__ == "__main__":
     demo.launch()