Create app.py

app.py

@@ -0,0 +1,189 @@
+import gradio as gr
+import os
+import numpy as np
+import faiss
+from mistralai import Mistral
+
+api_key = os.getenv("MISTRAL_API_KEY")
+client = Mistral(api_key=api_key)
+
+# =============================================================================
+# BASIC CHAT UI (Gradio Version)
+# =============================================================================
+
+def run_mistral_basic(message, history):
+    """Basic chat function for Gradio ChatInterface"""
+    messages = [{"role": "user", "content": message}]
+    chat_response = client.chat.complete(
+        model="mistral-large-latest", 
+        messages=messages
+    )
+    return chat_response.choices[0].message.content
+
+# Create basic chat interface
+basic_chat = gr.ChatInterface(
+    fn=run_mistral_basic,
+    title="Basic Mistral Chat",
+    description="Chat with Mistral AI"
+)
+
+# =============================================================================
+# RAG UI (Gradio Version)
+# =============================================================================
+
+# Global variable to store processed document
+processed_chunks = None
+faiss_index = None
+
+def get_text_embedding(input_text):
+    """Get embeddings from Mistral"""
+    embeddings_batch_response = client.embeddings.create(
+        model="mistral-embed", 
+        inputs=[input_text]
+    )
+    return embeddings_batch_response.data[0].embedding
+
+def process_document(file):
+    """Process uploaded document and create FAISS index"""
+    global processed_chunks, faiss_index
+    
+    if file is None:
+        return "Please upload a text file first."
+    
+    try:
+        # Read the file
+        with open(file.name, 'r', encoding='utf-8') as f:
+            text = f.read()
+        
+        # Split document into chunks
+        chunk_size = 2048
+        processed_chunks = [text[i:i + chunk_size] for i in range(0, len(text), chunk_size)]
+        
+        # Create embeddings and FAISS index
+        text_embeddings = np.array([get_text_embedding(chunk) for chunk in processed_chunks])
+        d = text_embeddings.shape[1]
+        faiss_index = faiss.IndexFlatL2(d)
+        faiss_index.add(text_embeddings.astype(np.float32))
+        
+        return f"Document processed successfully! Split into {len(processed_chunks)} chunks."
+    
+    except Exception as e:
+        return f"Error processing document: {str(e)}"
+
+def rag_chat(message, history):
+    """RAG chat function for Gradio"""
+    global processed_chunks, faiss_index
+    
+    if processed_chunks is None or faiss_index is None:
+        return "Please upload and process a document first."
+    
+    try:
+        # Create prompt template
+        prompt_template = """
+Context information is below.
+---------------------
+{retrieved_chunk}
+---------------------
+Given the context information and not prior knowledge, answer the query.
+Query: {question}
+Answer:
+"""
+        
+        # Get question embedding
+        question_embedding = np.array([get_text_embedding(message)])
+        
+        # Search for similar chunks
+        D, I = faiss_index.search(question_embedding.astype(np.float32), k=2)
+        retrieved_chunks = [processed_chunks[i] for i in I.tolist()[0]]
+        
+        # Generate response
+        prompt = prompt_template.format(
+            retrieved_chunk=retrieved_chunks, 
+            question=message
+        )
+        
+        messages = [{"role": "user", "content": prompt}]
+        chat_response = client.chat.complete(
+            model="mistral-large-latest",
+            messages=messages
+        )
+        
+        return chat_response.choices[0].message.content
+    
+    except Exception as e:
+        return f"Error generating response: {str(e)}"
+
+# =============================================================================
+# GRADIO INTERFACES
+# =============================================================================
+
+# Create RAG interface with file upload
+with gr.Blocks(title="RAG Chat with Mistral") as rag_interface:
+    gr.Markdown("# RAG Chat Interface")
+    gr.Markdown("Upload a text file and chat with its content!")
+    
+    with gr.Row():
+        file_upload = gr.File(
+            label="Upload Text File",
+            file_types=[".txt"],
+            type="filepath"
+        )
+        process_btn = gr.Button("Process Document", variant="primary")
+    
+    process_status = gr.Textbox(
+        label="Processing Status",
+        interactive=False,
+        placeholder="Upload a file and click 'Process Document'"
+    )
+    
+    # Chat interface
+    chatbot = gr.Chatbot(label="RAG Chat")
+    msg = gr.Textbox(
+        label="Your Message",
+        placeholder="Ask questions about the uploaded document...",
+        lines=2
+    )
+    
+    with gr.Row():
+        submit_btn = gr.Button("Send", variant="primary")
+        clear_btn = gr.Button("Clear Chat")
+    
+    # Event handlers
+    process_btn.click(
+        process_document,
+        inputs=[file_upload],
+        outputs=[process_status]
+    )
+    
+    def respond(message, chat_history):
+        if not message.strip():
+            return "", chat_history
+        
+        # Add user message to history
+        chat_history.append([message, None])
+        
+        # Get bot response
+        bot_response = rag_chat(message, chat_history)
+        
+        # Add bot response to history
+        chat_history[-1][1] = bot_response
+        
+        return "", chat_history
+    
+    submit_btn.click(
+        respond,
+        inputs=[msg, chatbot],
+        outputs=[msg, chatbot]
+    )
+    
+    msg.submit(
+        respond,
+        inputs=[msg, chatbot],
+        outputs=[msg, chatbot]
+    )
+    
+    clear_btn.click(lambda: ([], ""), outputs=[chatbot, msg])
+
+if __name__ == "__main__":
+    rag_interface.launch(share=True)
+