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NEYIRE/README.md

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+---
+title: Enterprise RAG Assistant
+emoji: 🚀
+colorFrom: blue
+colorTo: indigo
+sdk: gradio
+sdk_version: 4.0.0
+app_file: app.py
+pinned: false
+python_version: "3.10"
+app_port: 7860
+

NEYIRE/app.py

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+import gradio as gr
+from modules.code_assistant import CodeAssistant
+from modules.docs_assistant import DocsAssistant
+from modules.pdf_assistant import PDFAssistant
+from modules.utils import load_css
+
+def create_app():
+    # Initialize assistants
+    code_assistant = CodeAssistant()
+    docs_assistant = DocsAssistant()
+    pdf_assistant = PDFAssistant()
+    
+    with gr.Blocks(css=load_css()) as demo:
+        gr.Markdown("# Enterprise RAG Assistant")
+        
+        with gr.Tabs() as tabs:
+            # Code Assistant Tab
+            with gr.Tab("Code Assistant", id=1):
+                with gr.Row():
+                    with gr.Column():
+                        code_input = gr.Textbox(
+                            label="Ask coding questions",
+                            placeholder="Enter your coding question...",
+                            lines=3
+                        )
+                        code_submit = gr.Button("Get Code Solution")
+                    code_output = gr.Code(
+                        label="Code Output",
+                        language="python"
+                    )
+            
+            # Documentation Assistant Tab
+            with gr.Tab("Documentation Assistant", id=2):
+                with gr.Row():
+                    with gr.Column():
+                        docs_input = gr.Textbox(
+                            label="Documentation Query",
+                            placeholder="Ask about technical documentation...",
+                            lines=3
+                        )
+                        docs_file = gr.File(
+                            label="Upload Documentation",
+                            file_types=[".pdf", ".txt", ".md"]
+                        )
+                        docs_submit = gr.Button("Search Documentation")
+                    docs_output = gr.Markdown()
+            
+            # PDF RAG Assistant Tab
+            with gr.Tab("PDF Assistant", id=3):
+                with gr.Row():
+                    with gr.Column():
+                        pdf_file = gr.File(
+                            label="Upload PDF",
+                            file_types=[".pdf"]
+                        )
+                        pdf_query = gr.Textbox(
+                            label="Ask about the PDF",
+                            placeholder="Enter your question about the PDF...",
+                            lines=3
+                        )
+                        pdf_submit = gr.Button("Get Answer")
+                    pdf_output = gr.Markdown()
+        
+        # Event handlers
+        code_submit.click(
+            code_assistant.generate_response,
+            inputs=[code_input],
+            outputs=[code_output]
+        )
+        
+        docs_submit.click(
+            docs_assistant.search_docs,
+            inputs=[docs_input, docs_file],
+            outputs=[docs_output]
+        )
+        
+        pdf_submit.click(
+            pdf_assistant.answer_query,
+            inputs=[pdf_query, pdf_file],
+            outputs=[pdf_output]
+        )
+    
+    return demo
+
+if __name__ == "__main__":
+    app = create_app()
+    app.launch() 

NEYIRE/assets/style.css

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+/* Modern Enterprise Theme */
+.gradio-container {
+    font-family: 'Inter', sans-serif;
+    max-width: 1200px !important;
+    margin: auto;
+}
+
+.tabs {
+    background: #f8f9fa;
+    border-radius: 10px;
+    padding: 20px;
+}
+
+.input-box {
+    border: 1px solid #e0e0e0;
+    border-radius: 8px;
+    padding: 12px;
+}
+
+.button {
+    background: #2d63c8;
+    color: white;
+    border-radius: 6px;
+    padding: 10px 20px;
+    transition: all 0.3s ease;
+}
+
+.button:hover {
+    background: #1e4a9d;
+    transform: translateY(-1px);
+}
+
+.output-box {
+    background: #ffffff;
+    border: 1px solid #e0e0e0;
+    border-radius: 8px;
+    padding: 16px;
+    margin-top: 12px;
+} 

NEYIRE/modules/code_assistant.py

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+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+class CodeAssistant:
+    def __init__(self):
+        self.model_name = "Qwen/Qwen2.5-Coder-32B-Instruct"
+        self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
+        self.model = AutoModelForCausalLM.from_pretrained(
+            self.model_name,
+            torch_dtype=torch.bfloat16,
+            device_map="auto"
+        )
+    
+    def generate_response(self, query):
+        inputs = self.tokenizer(query, return_tensors="pt").to(self.model.device)
+        outputs = self.model.generate(
+            **inputs,
+            max_length=2048,
+            temperature=0.7,
+            top_p=0.95,
+            do_sample=True
+        )
+        return self.tokenizer.decode(outputs[0], skip_special_tokens=True) 

NEYIRE/modules/docs_assistant.py

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+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+class DocsAssistant:
+    def __init__(self):
+        self.model_name = "Arc53/docsgpt-40b-falcon"
+        self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
+        self.model = AutoModelForCausalLM.from_pretrained(
+            self.model_name,
+            torch_dtype=torch.bfloat16,
+            device_map="auto"
+        )
+    
+    def search_docs(self, query, doc_file):
+        # Process uploaded documentation
+        doc_content = self._process_doc_file(doc_file)
+        
+        # Create prompt with context
+        prompt = f"Documentation: {doc_content}\nQuery: {query}"
+        
+        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)
+        outputs = self.model.generate(
+            **inputs,
+            max_length=1024,
+            temperature=0.3,
+            top_p=0.95
+        )
+        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    def _process_doc_file(self, file):
+        # Implementation for file processing
+        pass 

NEYIRE/modules/pdf_assistant.py

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+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+from PyPDF2 import PdfReader
+
+class PDFAssistant:
+    def __init__(self):
+        self.model_name = "meta-llama/Llama-3.3-70B-Instruct"
+        self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
+        self.model = AutoModelForCausalLM.from_pretrained(
+            self.model_name,
+            torch_dtype=torch.bfloat16,
+            device_map="auto"
+        )
+    
+    def answer_query(self, query, pdf_file):
+        # Extract text from PDF
+        pdf_text = self._extract_pdf_text(pdf_file)
+        
+        # Create prompt with context
+        prompt = f"Context from PDF: {pdf_text}\nQuestion: {query}"
+        
+        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)
+        outputs = self.model.generate(
+            **inputs,
+            max_length=1024,
+            temperature=0.3,
+            top_p=0.95
+        )
+        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    def _extract_pdf_text(self, pdf_file):
+        reader = PdfReader(pdf_file)
+        text = ""
+        for page in reader.pages:
+            text += page.extract_text()
+        return text 

NEYIRE/modules/utils.py

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+ 

NEYIRE/requirements.txt

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+gradio>=4.0.0
+torch>=2.0.0
+transformers>=4.30.0
+pypdf2>=3.0.0
+sentencepiece
+accelerate