Create app.py

app.py

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+import gradio as gr
+from sentence_transformers import SentenceTransformer
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import faiss
+import numpy as np
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+import fitz  # PyMuPDF
+
+# Load models
+embed_model = SentenceTransformer("BAAI/bge-base-en-v1.5")
+
+model_id = "mistralai/Mistral-7B-Instruct-v0.1"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    load_in_4bit=True
+)
+llm = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+# Globals
+index = None
+doc_texts = []
+
+# PDF/Text extraction
+def extract_text(file):
+    if file.name.endswith(".pdf"):
+        text = ""
+        doc = fitz.open(file.name)
+        for page in doc:
+            text += page.get_text()
+        return text
+    elif file.name.endswith(".txt"):
+        return file.read().decode("utf-8")
+    else:
+        return "❌ Invalid file type."
+
+# File processing
+def process_file(file):
+    global index, doc_texts
+    text = extract_text(file)
+    if text.startswith("❌"):
+        return text
+
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=300, chunk_overlap=50)
+    doc_texts = text_splitter.split_text(text)
+    embeddings = embed_model.encode(doc_texts)
+
+    dim = embeddings.shape[1]
+    index = faiss.IndexFlatL2(dim)
+    index.add(np.array(embeddings))
+
+    return "✅ File processed successfully. You can now ask questions!"
+
+# Context + LLM response
+def generate_answer(question):
+    if index is None:
+        return "⚠️ Please upload and process a file first."
+    
+    question_embedding = embed_model.encode([question])
+    _, I = index.search(np.array(question_embedding), k=3)
+    context = "\n".join([doc_texts[i] for i in I[0]])
+
+    prompt = f"""[System: You are a helpful assistant. Answer strictly based on the context.]
+
+Context:
+{context}
+
+Question: {question}
+Answer:"""
+
+    result = llm(prompt, max_new_tokens=300, do_sample=True, temperature=0.7)
+    return result[0]["generated_text"].split("Answer:")[-1].strip()
+
+# Gradio UI
+with gr.Blocks(title="RAG Chatbot") as demo:
+    gr.Markdown("## 📚 RAG Chatbot - Upload PDF/TXT and Ask Questions")
+
+    with gr.Row():
+        file_input = gr.File(label="📁 Upload .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...")
+        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)
+
+demo.launch()