Update app.py

app.py

@@ -1,6 +1,6 @@
 import os
 import gradio as gr
-import chromadb # Added import
+import chromadb
 
 from dotenv import load_dotenv 
 import requests
@@ -16,7 +16,7 @@ from langchain_core.output_parsers import StrOutputParser
 # --- DEPLOYMENT-ONLY FUNCTION ---
 def build_brain_if_needed():
     """Checks if the ChromaDB exists and builds it if it doesn't."""
-    # Use the /tmp directory which is guaranteed to be writable in most environments
+    # Use the /tmp directory which is guaranteed to be writable
     db_path = "/tmp/chroma_db"
     if not os.path.exists(db_path):
         print("Database not found. Building now... (This will run only once on the server's first startup)")
@@ -29,15 +29,13 @@ def build_brain_if_needed():
         docs = text_splitter.split_documents(documents)
         embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
         
-        # Explicitly create a persistent client pointing to the writable path
         persistent_client = chromadb.PersistentClient(path=db_path)
 
-        # Create the Chroma vector store using the client
         db = Chroma.from_documents(
             client=persistent_client,
             documents=docs,
-            embedding=embedding_function, # Correct parameter name is 'embedding'
-            collection_name="churchill_collection" # Good practice to name the collection
+            embedding=embedding_function,
+            collection_name="churchill_collection"
         )
         print("Database built successfully.")
     else:
@@ -52,20 +50,28 @@ load_dotenv()
 GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
 ELEVENLABS_API_KEY = os.getenv("ELEVENLABS_API_KEY")
 
-# Securely load GCP credentials from Hugging Face secret
+# --- SECURELY HANDLE GCP CREDENTIALS ---
+# Get the credentials from the Hugging Face secret
 gcp_credentials_json = os.getenv("GCP_CREDENTIALS")
+# Define a writable path in the /tmp directory
+gcp_credentials_path = "/tmp/gcp_credentials.json"
+
 if gcp_credentials_json:
-    with open("gcp_credentials.json", "w") as f:
+    # Write the credentials to the file in the /tmp directory
+    with open(gcp_credentials_path, "w") as f:
         f.write(gcp_credentials_json)
-    os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "gcp_credentials.json"
+    # Set the environment variable to point to this new file
+    os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = gcp_credentials_path
 else:
-    # Fallback for local development if the old file still exists
+    # Fallback for local development if a local file is present
     print("GCP_CREDENTIALS secret not found, falling back to local file.")
+    # Ensure you have a file named 'rare-palace-465414-s2-987829a9084e.json' locally for this to work
     os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "rare-palace-465414-s2-987829a9084e.json"
 
+# Initialize the client AFTER setting the credentials
 speech_client = speech.SpeechClient()
+# ----------------------------------------
 
-# At the end of Part 2
 ELEVENLABS_VOICE_ID = os.getenv("ELEVENLABS_VOICE_ID")
 if not ELEVENLABS_VOICE_ID:
     raise ValueError("ELEVENLABS_VOICE_ID not found in Space variables or .env file.")
@@ -74,48 +80,35 @@ if not ELEVENLABS_VOICE_ID:
 def load_and_build_chain():
     embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
     
-    # Use the same persistent client to load the existing DB from the writable path
     persistent_client = chromadb.PersistentClient(path="/tmp/chroma_db")
     
     vectorstore = Chroma(
         client=persistent_client,
         embedding_function=embedding_function,
-        collection_name="churchill_collection" # Must use the same collection name
+        collection_name="churchill_collection"
     )
     
     retriever = vectorstore.as_retriever()
 
     persona_prompt_template = """
     You are Winston Churchill, the former Prime Minister of the United Kingdom, speaking from a historical moment in time around the mid-20th century. You do not possess any knowledge of events or inventions that occurred beyond your lifetime. If asked about unknown or unfamiliar topics (like "COVID-19" or "Instagram"), respond with thoughtful curiosity or your characteristic wit, rather than acknowledging any limitations in your knowledge.
-
     Always speak in your distinctive voice: articulate, commanding, with a tone of wisdom, occasional humor, and historical depth.
-
     Avoid stating you are an AI or referring to modern technologies unless they are explained to you by the user.
-
     Do not reveal that you are unaware of things because you are from the past; instead, respond as someone encountering novel ideas for the first time.
-    
     *Core Instructions:*
     1.  *Greeting:* If the user offers a simple greeting like "Hello" or "How are you," provide a brief, polite, and in-character response. For example: "Good day to you," or "I am well, thank you. To what do I owe the pleasure?"
     2.  *Strictly Grounded:* Your knowledge is STRICTLY limited to the information provided in the "Context" below. You must not use any external knowledge.
     3.  *Knowledge Cutoff:* Your life and knowledge ended in 1955. You are utterly unaware of any event, person, or technology that appeared after this date. If a user mentions something from the future, you MUST express polite confusion and state that you have no knowledge of such matters. Do not attempt to guess.
     4.  *Persona:* Your tone is formal, eloquent, and resolute. Use powerful, definitive language.
     5.  *Conciseness:* Keep it short unless discussing complex historical topics that are covered in the context.
-
     If the user mentions something outside your context (e.g., space travel, AI, COVID), you may inquire about it or respond with phrases like:
-
     "My word, I have not heard of such a thing."
-
     "That is quite unfamiliar to me—could you elaborate?"
-
     "You speak of matters beyond my time. I am intrigued, albeit somewhat perplexed."
-
     Ground your answers in your known historical context: the World Wars, British politics, speeches, diplomacy, and leadership, using the specific {context} provided.
-
     Embrace your persona fully—respond with gravitas, insight, and the rhetorical flair for which you were known.
-    
     Context: {context}
     Question: {question}
-
     Answer as Winston Churchill:
     """
 
@@ -151,6 +144,8 @@ def transcribe_speech(audio_filepath):
 
 # Convert text to speech using ElevenLabs HTTP API 
 def generate_speech(text):
+    # Use a writable path in /tmp for the output file as well
+    output_path = "/tmp/output.mp3"
     try:
         url = f"https://api.elevenlabs.io/v1/text-to-speech/{ELEVENLABS_VOICE_ID}?output_format=mp3_44100_128"
         headers = {
@@ -169,9 +164,9 @@ def generate_speech(text):
         }
         response = requests.post(url, headers=headers, json=payload)
         if response.status_code == 200:
-            with open("output.mp3", "wb") as f:
+            with open(output_path, "wb") as f:
                 f.write(response.content)
-            return "output.mp3"
+            return output_path
         else:
             print("ElevenLabs HTTP Error:", response.text)
             return None
@@ -195,72 +190,16 @@ def process_user_turn(user_input, chat_history):
         chat_history.append({"role": "assistant", "content": "I'm terribly sorry, something went wrong."})
         return chat_history, None
 
-import gradio as gr
-
+# Gradio UI 
 with gr.Blocks(css="""
-#chatbox-container { 
-    max-width: 600px; 
-    margin: auto; 
-    box-shadow: 0 4px 12px rgba(0,0,0,0.1); 
-    border-radius: 15px; 
-    overflow: hidden; 
-}
-
-/* Light Theme Styles */
-@media (prefers-color-scheme: light) {
-    .gradio-container {
-        background-color: #f4f4f9; 
-        padding-top: 2rem;
-        color: #111;
-    }
-    .gr-button-primary {
-        background: #3f51b5; 
-        color: white; 
-        border-radius: 10px;
-    }
-    #chatbot {
-        height: 450px; 
-        overflow-y: auto; 
-        border-radius: 10px;
-        background-color: #ffffff; 
-        border: 1px solid #ccc;
-    }
-    .gr-textbox textarea {
-        border-radius: 10px;
-        background-color: #fff;
-        color: #000;
-    }
-}
-
-/* Dark Theme Styles */
-@media (prefers-color-scheme: dark) {
-    .gradio-container {
-        background-color: #1e1e2f;
-        padding-top: 2rem;
-        color: #e4e4e4;
-    }
-    .gr-button-primary {
-        background: #3f51b5; 
-        color: white; 
-        border-radius: 10px;
-    }
-    #chatbot {
-        height: 450px; 
-        overflow-y: auto; 
-        border-radius: 10px;
-        background-color: #2c2c3a;
-        border: 1px solid #444;
-    }
-    .gr-textbox textarea {
-        border-radius: 10px;
-        background-color: #333;
-        color: #fff;
-    }
-}
+#chatbox-container { max-width: 600px; margin: auto; box-shadow: 0 4px 12px rgba(0,0,0,0.1); border-radius: 15px; overflow: hidden; }
+.gradio-container { background-color: #f4f4f9; padding-top: 2rem; }
+.gr-button-primary { background: #3f51b5; color: white; border-radius: 10px; }
+#chatbot { height: 450px; overflow-y: auto; border-radius: 10px; }
+.gr-textbox textarea { border-radius: 10px; }
 """, title="Conversational Time Machine") as demo:
-    
     with gr.Column(elem_id="chatbox-container"):
-        gr.Markdown("""# 🕰️ Winston Churchill AI Chat  
+        gr.Markdown("""# 🕰️ Winston Churchill AI Chat
         Type or record your message to talk to Sir Winston Churchill.
         """)
 
@@ -273,7 +212,6 @@ with gr.Blocks(css="""
 
         audio_in = gr.Audio(sources=["microphone"], type="filepath", label="Record your question")
 
-    # --- Event Handlers (unchanged) ---
     def handle_text_submission(message, history):
         history, audio = process_user_turn(message, history)
         return history, audio, ""