Create app1.py

app1.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")
+
+class MyApp:
+    def __init__(self) -> None:
+        self.documents = []
+        self.embeddings = None
+        self.index = None
+        self.load_pdf("YOURPDFFILE")
+        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')
+        # Generate embeddings for all document contents
+        self.embeddings = model.encode([doc["content"] for doc in self.documents])
+        # Create a FAISS index
+        self.index = faiss.IndexFlatL2(self.embeddings.shape[1])
+        # Add the embeddings to the index
+        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')
+        # Generate an embedding for the query
+        query_embedding = model.encode([query])
+        # Perform a search in the FAISS index
+        D, I = self.index.search(np.array(query_embedding), k)
+        # Retrieve the top-k documents
+        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 = "I offer mock interviews, personalized feedback, and valuable insights into common interview questions and industry-specific tips. My goal is to boost your confidence, improve your communication skills, and equip you with the tools needed to impress potential employers."
+    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": "coach", "content": val[1]})
+
+    messages.append({"role": "user", "content": message})
+
+    # RAG - Retrieve relevant documents
+    retrieved_docs = app.search_documents(message)
+    context = "\n".join(retrieved_docs)
+    messages.append({"role": "system", "content": "Relevant documents: " + context})
+
+    response = ""
+    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
+        yield response
+
+        demo = gr.Blocks()
+
+with demo:
+    gr.Markdown("**Job Interview Prep Coach**")
+    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=[
+            ["What are the most common mistakes candidates make during interviews, and how can I avoid them?"],
+        ["Do you have any tips for handling nerves or anxiety during interviews?"],
+        ["What are effective strategies for answering behavioral interview questions?"]
+        ],
+        title='Job Interview Prep Coach'
+    )
+
+if __name__ == "__main__":
+    demo.launch()