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| import os | |
| import pandas as pd | |
| import google.generativeai as genai | |
| import numpy as np | |
| import gradio as gr | |
| # Initialize an empty DataFrame with columns 'Title' and 'Text' | |
| df = pd.DataFrame(columns=['Title', 'Text']) | |
| # Mapping filenames to custom titles | |
| title_mapping = { | |
| 'company.txt': 'company_data', | |
| 'products.txt': 'product_data', | |
| 'shipping.txt': 'shipping_data' | |
| } | |
| # Loop through each file in the current directory and process the relevant ones | |
| for file_name in os.listdir('.'): | |
| if file_name in title_mapping: | |
| try: | |
| with open(file_name, 'r', encoding='utf-8') as file: | |
| text = file.read().replace('\n', ' ') # Replace newlines with spaces for cleaner text | |
| custom_title = title_mapping[file_name] | |
| new_row = pd.DataFrame({'Title': [custom_title], 'Text': [text]}) | |
| df = pd.concat([df, new_row], ignore_index=True) | |
| except Exception as e: | |
| print(f"Error processing file {file_name}: {e}") | |
| GEMINI_API_KEY = os.getenv("GOOGLE_API_KEY") | |
| if not GEMINI_API_KEY: | |
| return "Error: Gemini API key not found. Please set the GEMINI_API_KEY environment variable." | |
| # Configure the Gemini API | |
| try: | |
| genai.configure(api_key=GEMINI_API_KEY) | |
| except Exception as e: | |
| return f"Error: Failed to configure the Gemini API. Details: {e}" | |
| # Function to embed text using the Google Generative AI API | |
| def embed_text(text): | |
| return genai.embed_content(model='models/embedding-001', content=text, task_type='retrieval_document')['embedding'] | |
| # Embed the text and ensure embeddings are stored in the DataFrame | |
| if 'Embeddings' not in df.columns: | |
| df['Embeddings'] = df['Text'].apply(embed_text) | |
| # Function to calculate similarity score between the query and document embeddings | |
| def query_similarity_score(query, vector): | |
| query_embedding = embed_text(query) | |
| return np.dot(query_embedding, vector) | |
| # Function to get the most similar document based on the query | |
| def most_similar_document(query): | |
| # Create a local DataFrame copy to store similarity scores | |
| local_df = df.copy() | |
| # Calculate similarity for all rows | |
| local_df['Similarity'] = local_df['Embeddings'].apply(lambda vector: query_similarity_score(query, vector)) | |
| # Sort by similarity score and retrieve the top match | |
| most_similar = local_df.sort_values('Similarity', ascending=False).iloc[0] | |
| return most_similar['Title'], most_similar['Text'] | |
| # Function to generate a response using the RAG approach | |
| def RAG(query): | |
| title, text = most_similar_document(query) | |
| model = genai.GenerativeModel('gemini-pro') | |
| prompt = f"Answer this query:\n{query}.\nOnly use this context to answer:\n{text}" | |
| response = model.generate_content(prompt) | |
| return f"{response.text}\n\nSource Document: {title}" | |
| iface = gr.Interface( | |
| fn=RAG, # Main function to handle the query | |
| inputs=[ | |
| gr.Textbox(label="Enter Your Query"), # Input for the user's query | |
| ], | |
| outputs=gr.Textbox(label="Response"), # Output for the generated response | |
| title="Patrick's Multilingual Query Handler" | |
| ) | |