Update app.py

app.py

@@ -1,117 +1,55 @@
-from transformers import pipeline
-import firebase_admin
-from firebase_admin import credentials, db
 import os
 import json
-from firebase_admin import credentials
+import firebase_admin
+from firebase_admin import credentials, db
+from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration
+import gradio as gr
 
-# Fetch the Firebase credentials from the Hugging Face secret
+# Initialize Firebase Admin SDK
 firebase_credential = os.getenv("FIREBASE_CREDENTIALS")
+if not firebase_credential:
+    raise RuntimeError("FIREBASE_CREDENTIALS environment variable is not set.")
 
-# Save the credentials to a temporary file
+# Save Firebase credentials to a temporary file
 with open("serviceAccountKey.json", "w") as f:
     f.write(firebase_credential)
 
-# Initialize Firebase Admin SDK
+# Initialize Firebase App
 cred = credentials.Certificate("serviceAccountKey.json")
 firebase_admin.initialize_app(cred, {"databaseURL": "https://your-database-name.firebaseio.com/"})
 
-# ----------------------------------
-# Initialize Hugging Face RAG Model
-# ----------------------------------
-rag_model = pipeline("question-answering", model="facebook/rag-token-base")
-
-
-# ----------------------------------
-# Functions for Firebase Operations
-# ----------------------------------
-def fetch_tasks():
-    """Fetch tasks from Firebase Realtime Database."""
-    ref = db.reference("projects")
-    data = ref.get()
-    if not data:
-        return "No tasks found!"
-    output = ""
-    for project_id, project_data in data.items():
-        output += f"Project: {project_id}\n"
-        for task_id, task_data in project_data.get("tasks", {}).items():
-            output += f"- Task: {task_data['title']} (Status: {task_data['status']})\n"
-    return output
-
-
-def add_task(project_id, task_name, description, assignee, deadline):
-    """Add a new task to Firebase."""
-    ref = db.reference(f"projects/{project_id}/tasks")
-    new_task_ref = ref.push()  # Create a new task
-    new_task_ref.set(
-        {
-            "title": task_name,
-            "description": description,
-            "status": "in-progress",
-            "assignee": assignee,
-            "deadline": deadline,
-        }
-    )
-    return f"Task '{task_name}' added to project '{project_id}' successfully!"
-
-
-def query_rag(question):
-    """Query RAG model with project data as context."""
-    ref = db.reference("projects")
-    data = ref.get()
-    if not data:
-        return "No data available for RAG."
-
-    # Prepare context from Firebase data
-    context = ""
-    for project_id, project_data in data.items():
-        for task_id, task_data in project_data.get("tasks", {}).items():
-            context += f"{task_data['title']}: {task_data['status']} (Assigned to: {task_data['assignee']}).\n"
-
-    # Query RAG model
-    response = rag_model(question=question, context=context)
-    return response["answer"]
-
-
-# ----------------------------------
-# Gradio Dashboard
-# ----------------------------------
-def dashboard():
-    """Gradio interface for Project Management Dashboard."""
-    with gr.Blocks() as app:
-        gr.Markdown("## Project Management Dashboard with Live Updates")
-        
-        # Add Task Section
-        with gr.Tab("Add Task"):
-            project_id = gr.Textbox(label="Project ID")
-            task_name = gr.Textbox(label="Task Name")
-            description = gr.Textbox(label="Description")
-            assignee = gr.Textbox(label="Assignee")
-            deadline = gr.Textbox(label="Deadline (YYYY-MM-DD)")
-            add_task_button = gr.Button("Add Task")
-            add_task_output = gr.Textbox(label="Output", interactive=False)
-            add_task_button.click(
-                add_task,
-                [project_id, task_name, description, assignee, deadline],
-                add_task_output,
-            )
-
-        # Fetch Tasks Section
-        with gr.Tab("View Tasks"):
-            fetch_button = gr.Button("Fetch Tasks")
-            tasks_output = gr.Textbox(label="Tasks", interactive=False)
-            fetch_button.click(fetch_tasks, [], tasks_output)
-
-        # Query RAG Section
-        with gr.Tab("Ask Questions"):
-            question = gr.Textbox(label="Ask a question about your projects")
-            query_button = gr.Button("Get Answer")
-            rag_output = gr.Textbox(label="Answer", interactive=False)
-            query_button.click(query_rag, [question], rag_output)
-
-    app.launch()
-
-
-# Launch the app
+# Load the RAG model, tokenizer, and retriever
+tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-base")
+retriever = RagRetriever.from_pretrained("facebook/rag-token-base", use_dummy_dataset=True)  # Use a dummy dataset for now
+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
+    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"])
+    
+    # Generate the answer using the RAG model
+    outputs = model.generate(input_ids=inputs["input_ids"], 
+                             context_input_ids=retrieved_docs["context_input_ids"])
+    
+    # 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
 if __name__ == "__main__":
-    dashboard()
+    interface.launch()
+