Update app.py

app.py

@@ -1,186 +1,22 @@
 import os
-import faiss
-import numpy as np
-from sentence_transformers import SentenceTransformer
-from huggingface_hub import HfApi, hf_hub_download, login, whoami
-
-# 🔹 Hugging Face Repository Details
-HF_REPO_ID = "tstone87/repo"  # Your repo
-HF_TOKEN = os.getenv("HF_TOKEN")  # Retrieve token securely from environment variable
-
-if not HF_TOKEN:
-    raise ValueError("❌ ERROR: Hugging Face token not found. Add it as a secret in the Hugging Face Space settings.")
-
-# 🔹 Authenticate with Hugging Face
-login(token=HF_TOKEN)
-
-# 🔹 File Paths
-EMBEDDINGS_FILE = "policy_embeddings.npy"
-INDEX_FILE = "faiss_index.bin"
-TEXT_FILE = "combined_text_documents.txt"
-
-# 🔹 Load policy text from file
-if os.path.exists(TEXT_FILE):
-    with open(TEXT_FILE, "r", encoding="utf-8") as f:
-        POLICY_TEXT = f.read()
-    print("✅ Loaded policy text from combined_text_documents.txt")
-else:
-    print("❌ ERROR: combined_text_documents.txt not found! Ensure it's uploaded.")
-    POLICY_TEXT = ""
-
-# 🔹 Sentence Embedding Model (Optimized for Speed)
-model = SentenceTransformer("all-MiniLM-L6-v2")
-
-# 🔹 Split policy text into chunks for FAISS indexing
-chunk_size = 500
-chunks = [POLICY_TEXT[i:i+chunk_size] for i in range(0, len(POLICY_TEXT), chunk_size)] if POLICY_TEXT else []
-
-# 🔹 Function to Upload FAISS Files to Hugging Face Hub
-def upload_faiss_to_hf():
-    api = HfApi()
-
-    if os.path.exists(EMBEDDINGS_FILE):
-        print("📤 Uploading FAISS embeddings to Hugging Face...")
-        api.upload_file(
-            path_or_fileobj=EMBEDDINGS_FILE,
-            path_in_repo=EMBEDDINGS_FILE,
-            repo_id=HF_REPO_ID,
-            repo_type="dataset",
-            token=HF_TOKEN,
-        )
-
-    if os.path.exists(INDEX_FILE):
-        print("📤 Uploading FAISS index to Hugging Face...")
-        api.upload_file(
-            path_or_fileobj=INDEX_FILE,
-            path_in_repo=INDEX_FILE,
-            repo_id=HF_REPO_ID,
-            repo_type="dataset",
-            token=HF_TOKEN,
-        )
-
-    print("✅ FAISS files successfully uploaded to Hugging Face.")
-
-# 🔹 Function to Download FAISS Files from Hugging Face Hub if Missing
-def download_faiss_from_hf():
-    if not os.path.exists(EMBEDDINGS_FILE):
-        print("📥 Downloading FAISS embeddings from Hugging Face...")
-        hf_hub_download(repo_id=HF_REPO_ID, filename=EMBEDDINGS_FILE, local_dir=".", token=HF_TOKEN)
-
-    if not os.path.exists(INDEX_FILE):
-        print("📥 Downloading FAISS index from Hugging Face...")
-        hf_hub_download(repo_id=HF_REPO_ID, filename=INDEX_FILE, local_dir=".", token=HF_TOKEN)
-
-    print("✅ FAISS files downloaded from Hugging Face.")
-
-# 🔹 Check if FAISS Files Exist, Otherwise Download
-if os.path.exists(EMBEDDINGS_FILE) and os.path.exists(INDEX_FILE):
-    print("✅ FAISS files found locally. Loading from disk...")
-    embeddings = np.load(EMBEDDINGS_FILE)
-    index = faiss.read_index(INDEX_FILE)
-else:
-    print("🚀 FAISS files not found! Downloading from Hugging Face...")
-    download_faiss_from_hf()
-
-    if os.path.exists(EMBEDDINGS_FILE) and os.path.exists(INDEX_FILE):
-        embeddings = np.load(EMBEDDINGS_FILE)
-        index = faiss.read_index(INDEX_FILE)
-    else:
-        print("🚀 No FAISS files found. Recomputing...")
-        if chunks:
-            embeddings = np.array([model.encode(chunk) for chunk in chunks])
-
-            # Save embeddings for future use
-            np.save(EMBEDDINGS_FILE, embeddings)
-
-            # Use FAISS optimized index for faster lookup
-            d = embeddings.shape[1]
-            nlist = 10  # Number of clusters
-            index = faiss.IndexIVFFlat(faiss.IndexFlatL2(d), d, nlist)
-            index.train(embeddings)
-            index.add(embeddings)
-            index.nprobe = 2  # Speed optimization
-
-            # Save FAISS index
-            faiss.write_index(index, INDEX_FILE)
-            upload_faiss_to_hf()  # Upload FAISS files to Hugging Face
-            print("✅ FAISS index created and saved.")
-        else:
-            print("❌ ERROR: No text to index. Check combined_text_documents.txt.")
-            index = None
-
-# 🔹 Function to Search FAISS
-def search_policy(query, top_k=3):
-    if index is None:
-        return "Error: FAISS index is not available."
-
-    query_embedding = model.encode(query).reshape(1, -1)
-    distances, indices = index.search(query_embedding, top_k)
-
-    return "\n\n".join([chunks[i] for i in indices[0] if i < len(chunks)])
-
-# 🔹 Hugging Face LLM Client
-from huggingface_hub import InferenceClient
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-# 🔹 Function to Handle Chat Responses
-def respond(message, history, system_message, max_tokens, temperature, top_p):
-    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": "assistant", "content": val[1]})
-
-    # 🔹 Retrieve relevant policy info from FAISS
-    policy_context = search_policy(message)
-
-    if policy_context:
-        # 🔹 Display retrieved context in chat
-        messages.append({"role": "assistant", "content": f"📄 **Relevant Policy Context:**\n\n{policy_context}"})
-
-        # 🔹 Force the LLM to use the retrieved policy text
-        user_query_with_context = f"""
-        The following is the most relevant policy information retrieved from the official Colorado public assistance policies:
-
-        {policy_context}
+import shutil
+import gradio as gr
 
-        Based on this information, answer the following question:
-        {message}
-        """
-        messages.append({"role": "user", "content": user_query_with_context})
+# Function to prepare FAISS files for download
+def prepare_faiss_files():
+    if os.path.exists("policy_embeddings.npy") and os.path.exists("faiss_index.bin"):
+        shutil.copy("policy_embeddings.npy", "/mnt/data/policy_embeddings.npy")
+        shutil.copy("faiss_index.bin", "/mnt/data/faiss_index.bin")
+        return "✅ FAISS files are ready for download. Go to the 'Files' tab in Hugging Face Space and download them."
     else:
-        # If no relevant policy info is found, use the original message
-        messages.append({"role": "user", "content": message})
-
-    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
-
-# 🔹 Gradio Chat Interface
-import gradio as gr
+        return "❌ FAISS files not found. Try running the chatbot first to generate them."
 
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(
-            value="You are a knowledgeable and professional chatbot designed to assist Colorado case workers in determining eligibility for public assistance programs.",
-            label="System message"
-        ),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
-    ],
-)
+# Gradio UI to trigger FAISS file preparation
+with gr.Blocks() as download_ui:
+    gr.Markdown("### 🔽 Download FAISS Files")
+    download_button = gr.Button("Prepare FAISS Files for Download")
+    output_text = gr.Textbox()
+    download_button.click(fn=prepare_faiss_files, outputs=output_text)
 
-if __name__ == "__main__":
-    demo.launch()
+# Launch the download interface
+download_ui.launch()