Create app.py

app.py

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+import os
+import requests
+import fitz  # PyMuPDF for PDF reading
+import faiss
+import numpy as np
+import gradio as gr
+from sentence_transformers import SentenceTransformer
+from huggingface_hub import InferenceClient
+
+# 🔹 Define PDF Directory and Chunk Size
+PDF_DIR = "./pdfs"
+CHUNK_SIZE = 2500  # Larger chunks for better context
+
+# 🔹 Ensure Directory Exists
+os.makedirs(PDF_DIR, exist_ok=True)
+
+# 🔹 Direct URLs for PDF Downloads (Colorado Policy Documents)
+PDF_FILES = {
+    "SNAP 10 CCR 2506-1.pdf": "https://huggingface.co/spaces/tstone87/ccr-colorado/resolve/main/SNAP%2010%20CCR%202506-1%20.pdf?download=true",
+    "Med 10 CCR 2505-10 8.100.pdf": "https://huggingface.co/spaces/tstone87/ccr-colorado/resolve/main/Med%2010%20CCR%202505-10%208.100.pdf?download=true",
+}
+
+# 🔹 Function to Download PDFs
+
+def download_pdfs():
+    for filename, url in PDF_FILES.items():
+        pdf_path = os.path.join(PDF_DIR, filename)
+        if not os.path.exists(pdf_path):
+            print(f"📥 Downloading {filename}...")
+            try:
+                response = requests.get(url, stream=True)
+                response.raise_for_status()
+                with open(pdf_path, "wb") as f:
+                    for chunk in response.iter_content(chunk_size=8192):
+                        f.write(chunk)
+                print(f"✅ Downloaded {filename}")
+            except Exception as e:
+                print(f"❌ Error downloading {filename}: {e}")
+
+# 🔹 Function to Extract Text from PDFs
+
+def extract_text_from_pdfs():
+    all_text = ""
+    for pdf_file in os.listdir(PDF_DIR):
+        if pdf_file.endswith(".pdf"):
+            pdf_path = os.path.join(PDF_DIR, pdf_file)
+            doc = fitz.open(pdf_path)
+            for page in doc:
+                all_text += page.get_text("text") + "\n"
+    return all_text
+
+# 🔹 Initialize FAISS Index
+
+def initialize_faiss():
+    download_pdfs()
+    text_data = extract_text_from_pdfs()
+    if not text_data:
+        raise ValueError("❌ No text extracted from PDFs!")
+    
+    chunks = [text_data[i:i+CHUNK_SIZE] for i in range(0, len(text_data), CHUNK_SIZE)]
+    model = SentenceTransformer("multi-qa-mpnet-base-dot-v1")
+    embeddings = np.array([model.encode(chunk) for chunk in chunks])
+    index = faiss.IndexFlatL2(embeddings.shape[1])
+    index.add(embeddings)
+    print("✅ FAISS index initialized.")
+    return index, chunks
+
+# 🔹 Initialize FAISS on Startup
+index, chunks = initialize_faiss()
+
+# 🔹 Function to Search FAISS
+
+def search_policy(query, top_k=3):
+    query_embedding = SentenceTransformer("multi-qa-mpnet-base-dot-v1").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
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+
+# 🔹 Function to Handle Chat Responses
+
+def respond(message, history):
+    messages = [{"role": "system", "content": "You are a chatbot specializing in Colorado public assistance programs."}]
+    for val in history:
+        if val[0]:
+            messages.append({"role": "user", "content": val[0]})
+        if val[1]:
+            messages.append({"role": "assistant", "content": val[1]})
+    
+    policy_context = search_policy(message)
+    if policy_context:
+        messages.append({"role": "assistant", "content": f"📄 **Colorado Policy Info:**\n\n{policy_context}"})
+    
+    messages.append({"role": "user", "content": message})
+    response = ""
+    for message in client.chat_completion(messages, max_tokens=512, stream=True, temperature=0.7, top_p=0.95):
+        token = message.choices[0].delta.content
+        response += token
+        yield response
+
+# 🔹 Gradio Chat Interface (Colorado-Themed)
+demo = gr.ChatInterface(
+    respond,
+    textbox=gr.Textbox(placeholder="Ask about Colorado public assistance programs...", interactive=True, show_label=False),
+    submit_btn=gr.Button("Send"),
+    chatbot=gr.Chatbot(),
+)
+
+demo.launch()