Create app.py

app.py

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+# Vision 2030 Virtual Assistant with Arabic (ALLaM-7B) and English (Mistral-7B-Instruct) + RAG + Improved Prompting
+
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+from langdetect import detect
+from sentence_transformers import SentenceTransformer
+import faiss
+import numpy as np
+
+# ----------------------------
+# Load Arabic Model (ALLaM-7B)
+# ----------------------------
+print("Loading ALLaM-7B-Instruct-preview for Arabic...")
+arabic_model_id = "ALLaM-AI/ALLaM-7B-Instruct-preview"
+arabic_tokenizer = AutoTokenizer.from_pretrained(arabic_model_id)
+arabic_model = AutoModelForCausalLM.from_pretrained(arabic_model_id, device_map="auto")
+arabic_pipe = pipeline("text-generation", model=arabic_model, tokenizer=arabic_tokenizer)
+
+# ----------------------------
+# Load English Model (Mistral-7B-Instruct)
+# ----------------------------
+print("Loading Mistral-7B-Instruct-v0.2 for English...")
+english_model_id = "mistralai/Mistral-7B-Instruct-v0.2"
+english_tokenizer = AutoTokenizer.from_pretrained(english_model_id)
+english_model = AutoModelForCausalLM.from_pretrained(english_model_id, device_map="auto")
+english_pipe = pipeline("text-generation", model=english_model, tokenizer=english_tokenizer)
+
+# ----------------------------
+# Load Embedding Models for Retrieval
+# ----------------------------
+print("Loading Embedding Models for Retrieval...")
+arabic_embedder = SentenceTransformer('CAMeL-Lab/bert-base-arabic-camelbert-ca')
+english_embedder = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+
+# ----------------------------
+# Prepare FAISS Index (dummy example)
+# ----------------------------
+# In real scenario, load Vision 2030 documents, preprocess & embed
+# Here we'll create dummy data for demonstration
+
+documents = [
+    {"text": "Vision 2030 aims to diversify the Saudi economy.", "lang": "en"},
+    {"text": "رؤية 2030 تهدف إلى تنويع الاقتصاد السعودي.", "lang": "ar"}
+]
+
+# Embed documents and build index
+english_vectors = []
+arabic_vectors = []
+english_texts = []
+arabic_texts = []
+
+for doc in documents:
+    if doc["lang"] == "en":
+        vec = english_embedder.encode(doc["text"])
+        english_vectors.append(vec)
+        english_texts.append(doc["text"])
+    else:
+        vec = arabic_embedder.encode(doc["text"])
+        arabic_vectors.append(vec)
+        arabic_texts.append(doc["text"])
+
+# FAISS indexes
+english_index = faiss.IndexFlatL2(len(english_vectors[0]))
+english_index.add(np.array(english_vectors))
+
+arabic_index = faiss.IndexFlatL2(len(arabic_vectors[0]))
+arabic_index.add(np.array(arabic_vectors))
+
+# ----------------------------
+# Define the RAG response function with Improved Prompting
+# ----------------------------
+def retrieve_and_generate(user_input):
+    try:
+        lang = detect(user_input)
+    except:
+        lang = "en"  # Default fallback
+
+    if lang == "ar":
+        print("Detected Arabic input")
+        query_vec = arabic_embedder.encode(user_input)
+        D, I = arabic_index.search(np.array([query_vec]), k=1)
+        context = arabic_texts[I[0][0]] if I[0][0] >= 0 else ""
+        # Improved Arabic Prompt
+        input_text = (
+            f"أنت خبير في رؤية السعودية 2030.\n"
+            f"إليك بعض المعلومات المهمة:\n{context}\n\n"
+            f"مثال:\n"
+            f"السؤال: ما هي ركائز رؤية 2030؟\n"
+            f"الإجابة: ركائز رؤية 2030 هي مجتمع حيوي، اقتصاد مزدهر، ووطن طموح.\n\n"
+            f"أجب عن سؤال المستخدم بشكل واضح ودقيق.\n"
+            f"السؤال: {user_input}\n"
+            f"الإجابة:"
+        )
+        response = arabic_pipe(input_text, max_new_tokens=256, do_sample=True, temperature=0.7)
+        reply = response[0]['generated_text']
+
+    else:
+        print("Detected English input")
+        query_vec = english_embedder.encode(user_input)
+        D, I = english_index.search(np.array([query_vec]), k=1)
+        context = english_texts[I[0][0]] if I[0][0] >= 0 else ""
+        # Improved English Prompt
+        input_text = (
+            f"You are an expert on Saudi Arabia's Vision 2030.\n"
+            f"Here is some relevant information:\n{context}\n\n"
+            f"Example:\n"
+            f"Question: What are the key pillars of Vision 2030?\n"
+            f"Answer: The key pillars are a vibrant society, a thriving economy, and an ambitious nation.\n\n"
+            f"Answer the user's question clearly and accurately.\n"
+            f"Question: {user_input}\n"
+            f"Answer:"
+        )
+        response = english_pipe(input_text, max_new_tokens=256, do_sample=True, temperature=0.7)
+        reply = response[0]['generated_text']
+
+    return reply
+
+# ----------------------------
+# Gradio UI
+# ----------------------------
+with gr.Blocks() as demo:
+    gr.Markdown("# Vision 2030 Virtual Assistant 🌍\n\nSupports Arabic & English queries about Vision 2030 (with RAG retrieval and improved prompting).")
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(label="Ask me anything about Vision 2030")
+    clear = gr.Button("Clear")
+
+    def chat(message, history):
+        reply = retrieve_and_generate(message)
+        history.append((message, reply))
+        return history, ""
+
+    msg.submit(chat, [msg, chatbot], [chatbot, msg])
+    clear.click(lambda: None, None, chatbot, queue=False)
+
+# Launching the space
+demo.launch()