Create app.py

app.py

@@ -0,0 +1,85 @@
+import os
+import gradio as gr
+import google.generativeai as genai
+from typing import List, Tuple
+import fitz  # PyMuPDF
+from sentence_transformers import SentenceTransformer
+import numpy as np
+import faiss
+
+# Initialize Google API Key
+GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")
+genai.configure(api_key=GOOGLE_API_KEY)
+
+# 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 FAISS and SentenceTransformer embeddings."""
+        model = SentenceTransformer("all-MiniLM-L6-v2")
+        embeddings = model.encode([doc["content"] for doc in self.documents])
+        self.embeddings = np.array(embeddings, dtype="float32")
+        self.index = faiss.IndexFlatL2(self.embeddings.shape[1])
+        self.index.add(self.embeddings)
+        print("Vector database built successfully!")
+
+    def search(self, query: str, top_k: int = 5) -> List[Tuple[int, str]]:
+        """Searches for the most similar documents based on the query."""
+        query_embedding = SentenceTransformer("all-MiniLM-L6-v2").encode([query])
+        distances, indices = self.index.search(np.array(query_embedding, dtype="float32"), top_k)
+        return [(self.documents[idx]["page"], self.documents[idx]["content"]) for idx in indices[0]]
+
+    def generate_response(self, query: str) -> str:
+        """Generates a response using the Gemini model based on the query."""
+        if not GOOGLE_API_KEY:
+            raise ValueError("GOOGLE_API_KEY is not set. Please set it up.")
+
+        generation_config = genai.types.GenerationConfig(
+            temperature=0.7,
+            max_output_tokens=512
+        )
+
+        model_name = "gemini-1.5-pro-latest"
+        model = genai.GenerativeModel(model_name)
+        response = model.generate_content([query], generation_config=generation_config)
+        
+        return response[0].text if response else "No response generated."
+
+# Gradio UI setup for interaction
+def main():
+    app = MyApp()
+
+    def handle_query(query):
+        search_results = app.search(query)
+        response = app.generate_response(query)
+        return {"Search Results": search_results, "Response": response}
+
+    gr.Interface(
+        fn=handle_query,
+        inputs=gr.Textbox(placeholder="Enter your query here"),
+        outputs=[
+            gr.JSON(label="Search Results"),
+            gr.Textbox(label="Generated Response")
+        ],
+        title="Dialectical Behavioral Exercise with Gemini",
+        description="This app uses Google Gemini to generate responses based on document content."
+    ).launch()
+
+if __name__ == "__main__":
+    main()