Update app.py

app.py

@@ -1,64 +1,45 @@
-import os
-import json
-import firebase_admin
-from firebase_admin import credentials, db
-from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration
-from transformers import RagRetriever
+import torch
+from transformers import RagRetriever, RagTokenizer, RagSequenceForGeneration
+from datasets import load_dataset
 
+# Step 1: Load the dataset with the trust_remote_code flag enabled
+dataset = load_dataset("wiki_dpr", trust_remote_code=True)
+
+# Step 2: Load the retriever using the pre-trained model, with use_dummy_dataset=True and trust_remote_code=True
 retriever = RagRetriever.from_pretrained(
     "facebook/rag-token-base", 
     use_dummy_dataset=True, 
     trust_remote_code=True
 )
 
-import gradio as gr
-
-# Initialize Firebase Admin SDK
-firebase_credential = os.getenv("FIREBASE_CREDENTIALS")
-if not firebase_credential:
-    raise RuntimeError("FIREBASE_CREDENTIALS environment variable is not set.")
-
-# Save Firebase credentials to a temporary file
-with open("serviceAccountKey.json", "w") as f:
-    f.write(firebase_credential)
-
-# Initialize Firebase App
-cred = credentials.Certificate("serviceAccountKey.json")
-firebase_admin.initialize_app(cred, {"databaseURL": "https://your-database-name.firebaseio.com/"})
-
-# Load the RAG model, tokenizer, and retriever
+# Step 3: Load the tokenizer for the RAG model
 tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-base")
-retriever = RagRetriever.from_pretrained("facebook/rag-token-base", use_dummy_dataset=True, trust_remote_code=True)
+
+# Step 4: Initialize the RAG model
 model = RagSequenceForGeneration.from_pretrained("facebook/rag-token-base")
 
-# Function to generate answers using the RAG model
-def generate_answer(question, context=""):
-    # Tokenize the question and context
+# Step 5: Define a function to generate an answer using the retriever and model
+def generate_answer(question):
+    # Tokenize the question
     inputs = tokenizer(question, return_tensors="pt")
     
-    # Retrieve relevant documents (dummy dataset for this example)
-    # In a real-world case, you would provide a proper knowledge base or corpus
-    retrieved_docs = retriever(question=question, input_ids=inputs["input_ids"])
+    # Retrieve relevant documents using the retriever
+    input_ids = inputs["input_ids"]
+    retrieved_doc_ids = retriever.retrieve(input_ids)
     
-    # Generate the answer using the RAG model
-    outputs = model.generate(input_ids=inputs["input_ids"], 
-                             context_input_ids=retrieved_docs["context_input_ids"])
+    # Use the model to generate an answer based on the retrieved documents
+    generated_ids = model.generate(input_ids, context_input_ids=retrieved_doc_ids["context_input_ids"])
+    
+    # Decode the generated answer back to text
+    answer = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
     
-    # Decode the generated answer
-    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return answer
-
-# Gradio interface function
-def dashboard(question):
-    # Generate the answer from the RAG model
-    answer = generate_answer(question)
     return answer
 
-# Gradio Interface Setup
-interface = gr.Interface(fn=dashboard, inputs="text", outputs="text")
-
-# Launch the Gradio app
+# Step 6: Example usage
 if __name__ == "__main__":
-    interface.launch()
-
-
+    question = "Who was the first president of the United States?"
+    print(f"Question: {question}")
+    
+    # Generate and print the answer
+    answer = generate_answer(question)
+    print(f"Answer: {answer}")