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
+import numpy as np
+import faiss
+from gtts import gTTS
+import os
+from PIL import Image
+from moviepy.editor import ImageSequenceClip
+
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+
+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:
+        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:
+        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]:
+        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:
+    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:
+    messages = [{"role": "system", "content": "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 text_to_speech(text: str, lang: str = 'en'):
+    tts = gTTS(text=text, lang=lang, slow=False)
+    tts.save("response.mp3")
+    return "response.mp3"
+
+def create_speaking_avatar(image_path: str, audio_path: str):
+    # Use a simple way to generate a video where the image "speaks" the text
+    image = Image.open(image_path)
+    frames = [image] * 30  # 1 second at 30fps
+    clip = ImageSequenceClip([np.array(f) for f in frames], fps=30)
+    clip = clip.set_audio(audio_path)
+    output_path = "output.mp4"
+    clip.write_videofile(output_path, codec="libx264")
+    return output_path
+
+def respond(message: str, history: List[Tuple[str, str]]):
+    system_message = "You are a supportive and 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]})
+
+    messages.append({"role": "user", "content": message})
+
+    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))
+
+    # Convert response text to speech and create the speaking avatar
+    audio_path = text_to_speech(shortened_response)
+    avatar_video_path = create_speaking_avatar("avatar.png", audio_path)
+
+    return history, "", audio_path, avatar_video_path
+
+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 = [
+        ["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?"],
+        ["I want to practice mindfulness. Can you help me?"],
+    ]
+
+    gr.Examples(examples=example_questions, inputs=[txt_input])
+
+    submit_btn.click(fn=respond, inputs=[txt_input, chatbot], outputs=[chatbot, txt_input, gr.Audio(), gr.Video()])
+    refresh_btn.click(lambda: [], None, chatbot)
+
+if __name__ == "__main__":
+    demo.launch()