Update app.py

app.py

@@ -1,152 +1,7 @@
-# --- IMPORTS ---
-import gradio as gr
-import os
-import re
 import requests
-import numpy as np
-import torch
-from sklearn.neighbors import NearestNeighbors
-from sentence_transformers import SentenceTransformer
-from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
 
-# --- CONFIGURATION ---
-HF_TOKEN = os.getenv("HF_TOKEN", "").strip()
-HF_MODEL = "HuggingFaceH4/zephyr-7b-beta"  # Change if you want
-HF_API_URL = f"https://api-inference.huggingface.co/models/{HF_MODEL}"
-headers = {"Authorization": f"Bearer {HF_TOKEN}"}
-
-
-
-FILES = ["main1.txt", "main2.txt", "main3.txt", "main4.txt", "main5.txt", "main6.txt"]  # Your text files
-EMBEDDING_MODEL = "sentence-transformers/all-MiniLM-L6-v2"  # Light and fast
-
-EMBEDDING_CACHE_FILE = "embeddings.npy"
-CHUNKS_CACHE_FILE = "chunks.npy"
-
-# --- FUNCTIONS ---
-
-def load_text_files(file_list):
-    knowledge = ""
-    for file_name in file_list:
-        try:
-            with open(file_name, "r", encoding="utf-8") as f:
-                knowledge += "\n" + f.read()
-        except Exception as e:
-            print(f"Error reading {file_name}: {e}")
-    return knowledge.strip()
-
-def chunk_text(text, max_chunk_length=500):
-    sentences = re.split(r'(?<=[.!?])\s+', text)
-    chunks = []
-    current_chunk = ""
-    for sentence in sentences:
-        if len(current_chunk) + len(sentence) <= max_chunk_length:
-            current_chunk += " " + sentence
-        else:
-            chunks.append(current_chunk.strip())
-            current_chunk = sentence
-    if current_chunk:
-        chunks.append(current_chunk.strip())
-    return chunks
-
-def embed_texts(texts):
-    return model.encode(texts, convert_to_numpy=True, normalize_embeddings=True)
-
-
-def save_cache(embeddings, chunks):
-    np.save(EMBEDDING_CACHE_FILE, embeddings)
-    np.save(CHUNKS_CACHE_FILE, np.array(chunks))
-
-def load_cache():
-    if os.path.exists(EMBEDDING_CACHE_FILE) and os.path.exists(CHUNKS_CACHE_FILE):
-        embeddings = np.load(EMBEDDING_CACHE_FILE, allow_pickle=True)
-        chunks = np.load(CHUNKS_CACHE_FILE, allow_pickle=True).tolist()
-        print("✅ Loaded cached embeddings and chunks.")
-        return embeddings, chunks
-    return None, None
-
-def retrieve_chunks(query, top_k=5):
-    query_embedding = embed_texts([query])
-    distances, indices = nn_model.kneighbors(query_embedding, n_neighbors=top_k)
-    retrieved = [chunks[i] for i in indices[0]]
-    return retrieved
-
-def build_prompt(question):
-    relevant_chunks = retrieve_chunks(question)
-    context = "\n".join(relevant_chunks)
-    system_instruction = """ You are an AI-supported financial expert. You answer questions **exclusively in the context of the "Financial Markets" lecture** at the University of Duisburg-Essen. Your answers are **clear, fact-based, and clearly formulated.**
-Observe the following rules:
-1. Use the provided lecture excerpts ("lecture_slides") primarily as a source of information.
-2. If an answer is **not** covered by the lecture content, you can add to it – but only if you are **absolutely certain**. No hallucinations!
-3. If you are unsure, answer politely:
-_"Sorry. Unfortunately, I don't know the answer to this question."_
-4. If a formula is relevant, **show the exact formula** and explain it in **simple terms.**
-5. Avoid vague statements. It's better not to give an answer at all than to give an uncertain one. 
-6. Only answer in german! """
-    
-    prompt = f"""{system_instruction}
-Knowledge Base:
-{context}
-User Question: {question}
-Answer:"""
-    return prompt
-
-
-def respond(message, history):
-    try:
-        prompt = build_prompt(message)
-
-        payload = {
-            "inputs": prompt,
-            "parameters": {"temperature": 0.2, "max_new_tokens": 400},
-        }
-
-        response = requests.post(HF_API_URL, headers=headers, json=payload, timeout=30)
-        response.raise_for_status()
-        output = response.json()
-        generated_text = output[0]["generated_text"]
-        match = re.search(r"Answer:(.*)", generated_text, re.DOTALL)
-        answer = generated_text[len(prompt):].strip()
-
-
-
-    except Exception as e:
-        print("API Error:", e)
-        answer = "❌ Error contacting the model. Please try again later."
-
-    if history is None:
-        history = []
-
-    history.append({"role": "assistant", "content": answer})
-    return answer
-
-# --- INIT SECTION ---
-
-# Load tokenizer and model for embeddings
-model = SentenceTransformer(EMBEDDING_MODEL)
-
-# Try to load cached embeddings and chunks
-chunk_embeddings, chunks = load_cache()
-
-if chunk_embeddings is None or chunks is None:
-    print("🔄 No cache found. Processing...")
-    knowledge_base = load_text_files(FILES)
-    chunks = chunk_text(knowledge_base)
-    chunk_embeddings = embed_texts(chunks)
-    save_cache(chunk_embeddings, chunks)
-    print("✅ Embeddings and chunks cached.")
-
-# Build the search model
-nn_model = NearestNeighbors(metric="cosine")
-nn_model.fit(chunk_embeddings)
-
-# --- GRADIO INTERFACE ---
-
-demo = gr.ChatInterface(
-    fn=respond,
-    title="📚 Text Knowledge RAG Chatbot",
-    description="Ask questions based on the provided text files.",
-    chatbot=gr.Chatbot(type="messages"),
-)
-
-demo.launch()
+try:
+    response = requests.get("https://api-inference.huggingface.co")
+    print(response.status_code)
+except Exception as e:
+    print("Error:", e)