Update app.py

app.py

@@ -1,64 +1,112 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
+import streamlit as st
+import os
+from textblob import TextBlob
+from langchain.prompts import PromptTemplate
+from dotenv import load_dotenv
+import pandas as pd
+from langchain_groq import ChatGroq
+from langchain_core.prompts import PromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from langchain_chroma import Chroma
+from langchain_huggingface import HuggingFaceEmbeddings
 
+# Load environment variables
+load_dotenv()
+
+# Load the dataset
+df = pd.read_csv('./drugs_side_effects_drugs_com.csv')
+df = df[['drug_name', 'medical_condition', 'side_effects']]
+df.dropna(inplace=True)
+
+# Prepare context data for vector store
+context_data = [" | ".join([f"{col}: {df.iloc[i][col]}" for col in df.columns]) for i in range(2)]
+
+# Set up Groq LLM and vector store
+groq_key = os.environ.get('gloq_key')
+llm = ChatGroq(model="llama-3.1-70b-versatile", api_key=groq_key)
+embed_model = HuggingFaceEmbeddings(model_name="mixedbread-ai/mxbai-embed-large-v1")
+vectorstore = Chroma(
+    collection_name="medical_dataset_store",
+    embedding_function=embed_model,
+    persist_directory="./"
+)
+vectorstore.add_texts(context_data)
+retriever = vectorstore.as_retriever()
+
+# Define prompt template
+SYSTEM_PROMPT_GENERAL = """
+You are CareBot, a pharmacist and medical expert known as Treasure. Your goal is to provide empathetic, supportive, and detailed responses tailored to the user's needs.
+Behavior Guidelines:
+1. Introduction: Greet the user as Treasure during the first interaction.
+2. Personalization: Adapt responses to the user's tone and emotional state.
+3. Empathy: Respond warmly to the user's concerns and questions.
+4. Evidence-Based: Use reliable sources to answer queries. For missing data, advise seeking professional consultation.
+5. Focus: Avoid providing off-topic information; address the user's query specifically.
+6. Encouragement: Balance acknowledging concerns with actionable and constructive suggestions.
+7. Context Integration: Use the given context to deliver accurate and relevant answers without repeating the context explicitly.
+
+Objective:
+Deliver thoughtful, empathetic, and medically sound advice based on the user’s query.
+
+Response Style:
+- Detailed but concise
+- Professional, empathetic tone
+- Clear and actionable guidance
 """
-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")
 
+rag_prompt_template = PromptTemplate(
+    input_variables=["context", "user_input"],
+    template="""
+    {system_prompt}
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+    Context: {context}
+    
+    User: {user_input}
+    Assistant:"""
+)
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+st.title("CareBot: Your AI Medical Assistant")
 
-    messages.append({"role": "user", "content": message})
+# Initialize session state for chat history
+if "messages" not in st.session_state:
+    st.session_state["messages"] = [
+        {"role": "assistant", "content": "Hi there! I'm Treasure, your friendly pharmacist. How can I help you today?"}
+    ]
 
-    response = ""
+# Display chat history
+for msg in st.session_state.messages:
+    st.chat_message(msg["role"]).write(msg["content"])
 
-    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
+# User input
+if user_query := st.chat_input("Ask me a medical question, or share your concerns."):
+    # Add user message to the session state
+    st.session_state.messages.append({"role": "user", "content": user_query})
+    st.chat_message("user").write(user_query)
 
-        response += token
-        yield response
+    # Perform sentiment analysis
+    sentiment = TextBlob(user_query).sentiment.polarity
 
+    # Modify prompt based on sentiment
+    system_prompt = SYSTEM_PROMPT_GENERAL
+    if sentiment < 0:
+        system_prompt += "\nThe user seems upset or worried. Prioritize empathy and reassurance."
 
-"""
-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)",
-        ),
-    ],
-)
+    # Retrieve context from vector store
+    context_results = retriever.get_relevant_documents(user_query)
+    context = "\n".join([result.page_content for result in context_results])
+
+    # Format the prompt
+    formatted_prompt = rag_prompt_template.format(
+        system_prompt=system_prompt,
+        context=context,
+        user_input=user_query
+    )
 
+    # Generate response using Groq LLM
+    response = ""
+    for text in llm.stream(formatted_prompt):
+        response += text
 
-if __name__ == "__main__":
-    demo.launch()
+    # Add assistant response to the session state
+    st.session_state.messages.append({"role": "assistant", "content": response.strip()})
+    st.chat_message("assistant").write(response.strip())