Delete app.py

app.py

@@ -1,127 +0,0 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-from typing import List, Tuple
-import fitz  # PyMuPDF
-from sentence_transformers import SentenceTransformer, util
-import numpy as np
-import faiss
-
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-# Placeholder for the app's state
-class MyApp:
-    def __init__(self) -> None:
-        self.documents = []
-        self.embeddings = None
-        self.index = None
-        self.load_pdf("THEDIA1.pdf")
-        self.build_vector_db()
-
-    def load_pdf(self, file_path: str) -> None:
-        """Extracts text from a PDF file and stores it in the app's documents."""
-        doc = fitz.open(file_path)
-        self.documents = []
-        for page_num in range(len(doc)):
-            page = doc[page_num]
-            text = page.get_text()
-            self.documents.append({"page": page_num + 1, "content": text})
-        print("PDF processed successfully!")
-
-    def build_vector_db(self) -> None:
-        """Builds a vector database using the content of the PDF."""
-        model = SentenceTransformer('all-MiniLM-L6-v2')
-        self.embeddings = model.encode([doc["content"] for doc in self.documents])
-        self.index = faiss.IndexFlatL2(self.embeddings.shape[1])
-        self.index.add(np.array(self.embeddings))
-        print("Vector database built successfully!")
-
-    def search_documents(self, query: str, k: int = 3) -> List[str]:
-        """Searches for relevant documents using vector similarity."""
-        model = SentenceTransformer('all-MiniLM-L6-v2')
-        query_embedding = model.encode([query])
-        D, I = self.index.search(np.array(query_embedding), k)
-        results = [self.documents[i]["content"] for i in I[0]]
-        return results if results else ["No relevant documents found."]
-
-app = MyApp()
-
-def respond(
-    message: str,
-    history: List[Tuple[str, str]],
-    system_message: str,
-    max_tokens: int,
-    temperature: float,
-    top_p: float,
-):
-    system_message = (
-        "You are a knowledgeable DBT (Dialectical Behavior Therapy) coach. You greet users warmly and ask questions like a real counselor. "
-        "You are concise, respectful, and a good listener. You use the DBT book to guide users through DBT exercises and provide helpful information. "
-        "When needed, you ask one follow-up question at a time to guide the user to ask appropriate questions. "
-        "You avoid giving suggestions if any dangerous act is mentioned by the user and refer them to call someone or emergency services. "
-        "Your responses are accurate and concise, and you maintain a professional and supportive tone throughout."
-    )
-
-    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})
-
-    # RAG - Retrieve relevant documents
-    try:
-        retrieved_docs = app.search_documents(message)
-        context = "\n".join(retrieved_docs)
-        messages.append({"role": "system", "content": "Relevant documents: " + context})
-
-        response = ""
-        response_buffer = []
-        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
-            response_buffer.append(token)
-            if token.endswith('.') or token.endswith('?'):
-                yield ''.join(response_buffer)
-                response_buffer = []
-
-        if response_buffer:
-            yield ''.join(response_buffer)
-
-    except Exception as e:
-        yield f"An error occurred: {str(e)}"
-
-demo = gr.Blocks()
-
-with demo:
-    gr.Markdown("🧘‍♀️ **Dialectical Behaviour Therapy**")
-    gr.Markdown(
-        "‼️Disclaimer: This chatbot is based on a DBT exercise book that is publicly available. "
-        "We are not medical practitioners, and the use of this chatbot is at your own responsibility.‼️"
-    )
-    
-    chatbot = gr.ChatInterface(
-        respond,
-        examples=[
-            ["I feel overwhelmed with work."],
-            ["Can you guide me through a quick meditation?"],
-            ["How do I stop worrying about things I can't control?"],
-            ["What are some DBT skills for managing anxiety?"],
-            ["Can you explain mindfulness in DBT?"],
-            ["I am interested in DBT exercises"],
-            ["I feel restless. Please help me."],
-            ["I have destructive thoughts coming to my mind repetitively."]
-        ],
-        title='Dialectical Behaviour Therapy Assistant👩‍⚕️'
-    )
-
-if __name__ == "__main__":
-    demo.launch()