Delete app.py

app.py

@@ -1,110 +0,0 @@
-import pandas as pd
-import os
-from sentence_transformers import SentenceTransformer
-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
-import logging
-
-# Set up basic logging (optional, but useful)
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-
-
-try:
-    # Load the data - check for the file path
-    df = pd.read_csv('./Mental_Health_FAQ.csv')
-
-    context_data = []
-    for i in range(len(df)):
-      context = f"Question: {df.iloc[i]['Questions']} Answer: {df.iloc[i]['Answers']}"
-      context_data.append(context)
-
-    # Embed the contexts
-    embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
-    context_embeddings = embedding_model.encode(context_data)
-
-
-    # Get the API Key - important to check this is set
-    groq_key = os.environ.get('new_chatAPI_key')
-    if not groq_key:
-        raise ValueError("Groq API key not found in environment variables.")
-
-
-    # LLM used for RAG
-    llm = ChatGroq(model="llama-3.3-70b-versatile",api_key=groq_key)
-
-    # Embedding model
-    embed_model = HuggingFaceEmbeddings(model_name="mixedbread-ai/mxbai-embed-large-v1")
-
-
-    # Create the Vector Store!
-    vectorstore = Chroma(
-        collection_name="medical_dataset_store",
-        embedding_function=embed_model,
-    )
-
-    # Add data to vector store
-    vectorstore.add_texts(context_data)
-
-    retriever = vectorstore.as_retriever()
-
-    # Create the prompt template
-    template = ("""You are a mental health professional.
-        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)
-    rag_chain = (
-        {"context": retriever, "question": RunnablePassthrough()}
-        | rag_prompt
-        | llm
-        | 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 = [
-        "I am not in a good mood", 
-        "what is the possible symptoms of depression?"
-    ]
-
-    description = "This is for learning purpose"
-    title = "ThriveTalk Expert :) Try me!"
-    demo = gr.ChatInterface(fn=rag_memory_stream,
-                            type="messages",
-                            title=title,
-                            description=description,
-                            fill_height=True,
-                            examples=examples,
-                            theme="glass",
-    )
-
-except Exception as e:
-    logging.error(f"An error occurred during initialization: {e}")
-    # If there is an error then return a dummy error text to tell user
-    def error_function(message, history):
-         yield "An error has occurred. Please check the logs"
-    demo = gr.ChatInterface(fn=error_function,
-                         type="messages",
-                         title="ThriveTalk :(Ask me",
-                         description="Please check the logs",
-                         fill_height=True,
-                         theme="glass",
-    )
-
-
-if __name__ == "__main__":
-    demo.launch()