Create app.py

app.py

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+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 preprocess_input(user_input: str) -> str:
+    """Preprocesses user input to enhance it for better context."""
+    if "therapy" in user_input.lower():
+        return "I am looking for guidance on therapy. Can you help me with some exercises or techniques to manage my stress and emotions?"
+    # Add more rules as needed
+    return user_input
+
+def preprocess_response(response: str) -> str:
+    """Preprocesses the response to make it more polished."""
+    response = response.strip()
+    response = response.replace("\n\n", "\n")
+    response = response.replace(" ,", ",")
+    response = response.replace(" .", ".")
+    response = " ".join(response.split())
+    return response
+
+def shorten_response(response: str) -> str:
+    """Uses the Zephyr model to shorten and refine the response."""
+    messages = [{"role": "system", "content": "Shorten and refine this response."}, {"role": "user", "content": response}]
+    result = client.chat_completion(messages, max_tokens=256, 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 = "You are a concisely speaking empathetic Dialectical Behaviour Therapist assistant. You politely guide users through DBT exercises based on the given DBT book. You must say one thing at a time and ask follow-up questions to continue the chat."
+    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]})
+
+    # Preprocess user input
+    preprocessed_message = preprocess_input(message)
+    messages.append({"role": "user", "content": preprocessed_message})
+
+    # RAG - Retrieve relevant documents
+    retrieved_docs = app.search_documents(preprocessed_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():
+        txt_input = gr.Textbox(
+            show_label=False,
+            placeholder="Type your message here...",
+            lines=1
+        )
+        submit_btn = gr.Button("Submit", scale=1)
+        refresh_btn = gr.Button("Refresh Chat", scale=1, variant="secondary")
+
+    example_questions = [
+        ["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?"],
+        ["What is radical acceptance?"],
+        ["How can I practice distress tolerance?"],
+        ["What are some techniques to handle distressing situations?"],
+        ["How does DBT help with emotional regulation?"],
+        ["Can you give me an example of an interpersonal effectiveness skill?"]
+    ]
+
+    gr.Examples(examples=example_questions, inputs=[txt_input])
+
+    submit_btn.click(respond, [txt_input, chatbot], [chatbot, txt_input])
+    refresh_btn.click(lambda: [], None, chatbot)
+
+if __name__ == "__main__":
+    demo.launch()