Update app.py

app.py

@@ -9,31 +9,31 @@ from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from huggingface_hub import login
 
-# 1. Authenticate HuggingFace
+# Authenticate with Hugging Face
 hf_token = os.environ.get("HUGGINGFACE_TOKEN")
 if not hf_token:
     raise ValueError("⚠️ Please set the HUGGINGFACE_TOKEN environment variable.")
 login(token=hf_token)
 
-# 2. Load embedding model
+# Load embedding model
 embed_model = SentenceTransformer("BAAI/bge-base-en-v1.5")
 
-# 3. Load LLM (Mistral 7B Instruct with 4-bit quantization)
+# Load Mistral without 4bit quantization (CPU-friendly)
 model_id = "mistralai/Mistral-7B-Instruct-v0.1"
-tokenizer = AutoTokenizer.from_pretrained(model_id, use_auth_token=hf_token)
+tokenizer = AutoTokenizer.from_pretrained(model_id, token=hf_token)
 model = AutoModelForCausalLM.from_pretrained(
     model_id,
-    device_map="auto",
-    load_in_4bit=True,
-    use_auth_token=hf_token
+    device_map={"": "cpu"},  # force CPU
+    torch_dtype="auto",      # safe for CPU
+    token=hf_token
 )
 llm = pipeline("text-generation", model=model, tokenizer=tokenizer)
 
-# 4. Globals
+# Globals
 index = None
 doc_texts = []
 
-# 5. Extract text from uploaded file
+# Extract text from PDF or TXT
 def extract_text(file):
     text = ""
     file_bytes = file.read()
@@ -45,69 +45,60 @@ def extract_text(file):
     elif file.name.endswith(".txt"):
         text = file_bytes.decode("utf-8")
     else:
-        return "❌ Unsupported file type. Only PDF and TXT are allowed."
+        return "❌ Unsupported file type."
     return text
 
-# 6. Process the file: split text, create embeddings, build FAISS index
+# Process the file, build FAISS index
 def process_file(file):
     global index, doc_texts
     text = extract_text(file)
     if text.startswith("❌"):
         return text
 
-    # Split text
     splitter = RecursiveCharacterTextSplitter(chunk_size=300, chunk_overlap=50)
     doc_texts = splitter.split_text(text)
 
-    # Create embeddings
     embeddings = embed_model.encode(doc_texts, convert_to_numpy=True)
-
-    # Build FAISS index
     dim = embeddings.shape[1]
     index = faiss.IndexFlatL2(dim)
     index.add(embeddings)
 
-    return "✅ File processed successfully. You can now ask questions!"
+    return "✅ File processed! You can now ask questions."
 
-# 7. Generate answer based on question + retrieved context
+# Generate answer using context + LLM
 def generate_answer(question):
     global index, doc_texts
     if index is None or not doc_texts:
         return "⚠️ Please upload and process a file first."
 
-    # Embed the question
     question_emb = embed_model.encode([question], convert_to_numpy=True)
     _, I = index.search(question_emb, k=3)
-
-    # Build context
     context = "\n".join([doc_texts[i] for i in I[0]])
 
-    # Prompt
-    prompt = f"""[System: You are a helpful assistant. Answer strictly based on the context. Do not hallucinate.]
+    prompt = f"""[System: You are a helpful assistant. Answer based on the context.]
+
 Context:
 {context}
 
 Question: {question}
 Answer:"""
 
-    # Generate response
     response = llm(prompt, max_new_tokens=300, do_sample=True, temperature=0.7)
     return response[0]["generated_text"].split("Answer:")[-1].strip()
 
-# 8. Gradio UI
-with gr.Blocks(title="🧠 RAG Chatbot") as demo:
-    gr.Markdown("## 📚 Retrieval-Augmented Generation Chatbot\nUpload a `.pdf` or `.txt` and ask questions from the content.")
+# Gradio UI
+with gr.Blocks(title="RAG Chatbot (CPU Compatible)") as demo:
+    gr.Markdown("## 📚 Upload PDF/TXT and Ask Questions using Mistral-7B")
 
     with gr.Row():
-        file_input = gr.File(label="📁 Upload PDF/TXT", file_types=[".pdf", ".txt"])
-        upload_status = gr.Textbox(label="📥 Upload Status", interactive=False)
+        file_input = gr.File(label="📁 Upload File (.pdf or .txt)", file_types=[".pdf", ".txt"])
+        upload_status = gr.Textbox(label="Upload Status", interactive=False)
 
     with gr.Row():
-        question_box = gr.Textbox(label="❓ Ask a Question", placeholder="Type your question here...")
+        question_box = gr.Textbox(label="❓ Ask a Question", placeholder="What would you like to know?")
         answer_box = gr.Textbox(label="💬 Answer", interactive=False)
 
     file_input.change(fn=process_file, inputs=file_input, outputs=upload_status)
     question_box.submit(fn=generate_answer, inputs=question_box, outputs=answer_box)
 
-# 9. Launch the app
 demo.launch()