Delete app.py

app.py

@@ -1,139 +0,0 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-from typing import List, Tuple
-import fitz  # PyMuPDF
-from sentence_transformers import SentenceTransformer
-import numpy as np
-import faiss
-
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-#client = InferenceClient("meta-llama/Llama-2-7b-chat-hf")
-
-# Placeholder for the app's state
-class MyApp:
-    def __init__(self) -> None:
-        self.documents = []
-        self.embeddings = None
-        self.index = None
-
-    def load_pdfs(self, file_paths: List[str]) -> None:
-        """Extracts text from multiple PDF files and stores it in the app's documents."""
-        self.documents = []
-        for file_path in file_paths:
-            doc = fitz.open(file_path)
-            for page_num in range(len(doc)):
-                page = doc[page_num]
-                text = page.get_text()
-                self.documents.append({"page": page_num + 1, "content": text, "file": file_path})
-        print("PDFs processed successfully!")
-
-    def build_vector_db(self) -> None:
-        """Builds a vector database using the content of the PDFs."""
-        model = SentenceTransformer('all-MiniLM-L6-v2')
-        self.embeddings = model.encode([doc["content"] for doc in self.documents], show_progress_bar=True)
-        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], show_progress_bar=False)
-        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 preprocess_response(response: str) -> str:
-    """Preprocesses the response to make it more polished and empathetic."""
-    response = response.strip()
-    response = response.replace("\n\n", "\n")
-    response = response.replace(" ,", ",")
-    response = response.replace(" .", ".")
-    response = " ".join(response.split())
-    if not any(word in response.lower() for word in ["sorry", "apologize", "empathy"]):
-        response = "I'm here to help. " + response
-    return response
-
-def shorten_response(response: str) -> str:
-    """Uses the Zephyr model to shorten and refine the response."""
-    messages = [{"role": "system", "content": "Greet, Shorten and refine this response in a supportive and empathetic manner."}, {"role": "user", "content": response}]
-    result = client.chat_completion(messages, max_tokens=512, temperature=0.5, top_p=0.9)
-    return result.choices[0].message['content'].strip()
-
-def respond(message: str, history: List[Tuple[str, str]], system_message: str):
-    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 if the query suggests exercises or specific information
-    if any(keyword in message.lower() for keyword in ["exercise", "technique", "information", "guide", "help", "how to"]):
-        retrieved_docs = app.search_documents(message)
-        context = "\n".join(retrieved_docs)
-        if context.strip():
-            messages.append({"role": "system", "content": "Relevant documents: " + context})
-
-    response = client.chat_completion(messages, max_tokens=1024, temperature=0.7, top_p=0.9)
-    response_content = "".join([choice.message['content'] for choice in response.choices if 'content' in choice.message])
-
-    polished_response = preprocess_response(response_content)
-    shortened_response = shorten_response(polished_response)
-
-    history.append((message, shortened_response))
-    return history, ""
-
-with gr.Blocks() as 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.Chatbot()
-
-    with gr.Row():
-        pdf_input = gr.File(label="Upload PDFs (minimum 5)", file_count="multiple", type="file")
-        txt_input = gr.Textbox(
-            show_label=False,
-            placeholder="Type your message here...",
-            lines=1
-        )
-        system_input = gr.Textbox(
-            label="System Message",
-            placeholder="Enter a system message here...",
-            lines=2
-        )
-        submit_btn = gr.Button("Submit", scale=1)
-        refresh_btn = gr.Button("Refresh Chat", scale=1, variant="secondary")
-
-    example_questions = [
-        ["What are some ways to cope with stress using DBT?"],
-        ["Can you guide me through a grounding exercise?"],
-        ["How do I use DBT skills to handle intense emotions?"],
-        ["What are some self-soothing techniques I can practice?"]
-    ]
-
-    gr.Examples(examples=example_questions, inputs=[txt_input])
-
-    def load_and_build_pdfs(pdfs):
-        file_paths = [pdf.name for pdf in pdfs]
-        if len(file_paths) < 5:
-            return [], "Please upload at least 5 PDFs."
-        app.load_pdfs(file_paths)
-        app.build_vector_db()
-        return []
-
-    submit_btn.click(fn=load_and_build_pdfs, inputs=[pdf_input], outputs=[])
-
-    submit_btn.click(fn=respond, inputs=[txt_input, chatbot, system_input], outputs=[chatbot, txt_input])
-    refresh_btn.click(lambda: [], None, chatbot)
-
-if __name__ == "__main__":
-    demo.launch()