import os from huggingface_hub import login, hf_hub_download import pandas as pd import gradio as gr from llama_cpp import Llama import chromadb from sentence_transformers import SentenceTransformer from deep_translator import GoogleTranslator # Changed from googletrans to deep_translator import re import requests # Import the requests library # Charger le token depuis les secrets hf_token = os.getenv("HF_TOKEN") login(token=hf_token) # Charger le dataset depuis un fichier CSV local csv_file = "/content/indian_food (1).csv" try: df = pd.read_csv(csv_file) print("Dataset chargé avec succès depuis le fichier CSV local.") except FileNotFoundError: print(f"Erreur: Fichier CSV non trouvé à l'emplacement: {csv_file}") exit() except Exception as e: print(f"Erreur lors du chargement du CSV: {e}") exit() # Initialisation du modèle Llama llm = None # Initialize to None try: # Use /tmp for the model path within Hugging Face Spaces model_path = hf_hub_download( repo_id="TheBloke/CapybaraHermes-2.5-Mistral-7B-GGUF", filename="capybarahermes-2.5-mistral-7b.Q2_K.gguf", cache_dir="/tmp" # Store the model in /tmp ) llm = Llama( model_path=model_path, n_ctx=2048, ) print("Llama model loaded successfully.") except Exception as e: print(f"Error loading Llama model: {e}") # Initialisation de ChromaDB Vector Store class VectorStore: def __init__(self, collection_name): self.embedding_model = SentenceTransformer('sentence-transformers/multi-qa-MiniLM-L6-cos-v1') self.chroma_client = chromadb.Client() if collection_name in self.chroma_client.list_collections(): self.chroma_client.delete_collection(collection_name) self.collection = self.chroma_client.create_collection(name=collection_name) def populate_vectors(self, df): titles = df['name'].tolist() ingredients = df['ingredients'].tolist() diets = df['diet'].tolist() prep_times = df['prep_time'].tolist() # Load nutritional information, handling potentially missing columns and types calories = df['calories'].astype(str).tolist() if 'calories' in df else ['None'] * len(df) sugar = df['sugar'].astype(str).tolist() if 'sugar' in df else ['None'] * len(df) gluten = df['gluten'].astype(str).tolist() if 'gluten' in df else ['None'] * len(df) titles = titles[:2000] ingredients = ingredients[:2000] diets = diets[:2000] prep_times = prep_times[:2000] calories = calories[:2000] sugar = sugar[:2000] gluten = gluten[:2000] texts = [ f"Recipe: {title}. Ingredients: {ingredient}. Diet: {diet}. Prep Time: {prep_time} minutes. Calories: {calorie}. Sugar: {sugar}. Gluten: {gluten}." for title, ingredient, diet, prep_time, calorie, sugar, gluten in zip(titles, ingredients, diets, prep_times, calories, sugar, gluten) ] for i, item in enumerate(texts): embeddings = self.embedding_model.encode(item).tolist() self.collection.add(embeddings=[embeddings], documents=[item], ids=[str(i)]) def search_context(self, query, n_results=1): query_embedding = self.embedding_model.encode([query]).tolist() results = self.collection.query(query_embeddings=query_embedding, n_results=n_results) return results['documents'] # Initialisation du store de vecteurs et peuplement vector_store = None # Initialize to None try: vector_store = VectorStore("indian_food_embedding") vector_store.populate_vectors(df) print("Vector store initialized and populated.") except Exception as e: print(f"Error initializing or populating vector store: {e}") # Replace the translate_text function with this new version def translate_text(text, target_language='en'): """Translates the given text to the target language.""" try: if target_language == 'en': translator = GoogleTranslator(source='auto', target='en') else: translator = GoogleTranslator(source='en', target=target_language) translated_text = translator.translate(text) return translated_text except Exception as e: print(f"Translation error: {e}") print(f"Detailed error: {type(e).__name__}, {e}") # Print more details for debugging. return text # Return original text if translation fails def generate_text(message, max_tokens=600, temperature=0.3, top_p=0.95, gluten_free=False, dairy_free=False, allergies="", input_language='en'): # Added input_language if llm is None: return "Error: Llama model could not be loaded. Please check the console for errors." if vector_store is None: return "Error: Vector store could not be initialized. Please check the console for errors." # Translate the input message to English message_en = message if input_language != 'en': try: message_en = translate_text(message, target_language='en') except Exception as e: print(f"Error translating input message: {e}") return "Error translating input. Please try again in English." context = "" query = message_en if gluten_free: query += " gluten-free" if dairy_free: query += " dairy-free" if allergies: query += f" avoid ingredients: {allergies}" try: context_results = vector_store.search_context(query, n_results=1) if context_results and isinstance(context_results, list): context = context_results[0] if context_results else "" else: context = "" # or handle the error appropriately print("Warning: No context found or invalid context format.") except Exception as e: return f"Error searching vector store: {e}" prompt_template = ( f"SYSTEM: You are a helpful recipe generating bot specializing in Indian cuisine, assisting with dietary restrictions.\n" f"SYSTEM: Here is some context:\n{context}\n" f"USER: {message_en}\n" # Use the English translated message f"ASSISTANT:\n" ) try: output = llm( prompt_template, temperature=temperature, top_p=top_p, top_k=40, repeat_penalty=1.1, max_tokens=max_tokens, ) input_string = output['choices'][0]['text'].strip() cleaned_text = input_string.strip("[]'").replace('\\n', '\n') continuous_text = '\n'.join(cleaned_text.split('\n')) # Translate the output back to the input language output_text = continuous_text if input_language != 'en': try: output_text = translate_text(continuous_text, target_language=input_language) except Exception as e: print(f"Error translating output message: {e}") output_text = "Error translating output. Here is the English version:\n\n" + continuous_text # Gluten Check on Output if context and isinstance(context, str): context_lower = context.lower() if "gluten: yes" in context_lower: output_text += "\n\nWarning: This recipe contains gluten." elif "gluten: no" in context_lower: output_text += "\n\nGood news! This recipe is gluten-free." return output_text except Exception as e: return f"Error generating text: {e}" demo = gr.Interface( fn=generate_text, inputs=[ gr.Textbox(lines=2, placeholder="Enter your message here...", label="Message"), gr.Slider(minimum=50, maximum=1000, value=600, step=50, label="Max Tokens"), gr.Slider(minimum=0.1, maximum=1.0, value=0.3, step=0.1, label="Temperature"), gr.Slider(minimum=0.7, maximum=1.0, value=0.95, step=0.05, label="Top P"), gr.Checkbox(label="Gluten-Free"), gr.Checkbox(label="Dairy-Free"), gr.Textbox(lines=1, placeholder="e.g., peanuts, shellfish", label="Allergies (comma-separated)"), gr.Dropdown(choices=['en', 'hi'], value='en', label="Input Language (en=English, hi=Hindi/Hinglish)"), # Added language selection ], outputs=gr.Textbox(label="Generated Text"), title="Indian Recipe Bot", description="Running LLM with context retrieval from ChromaDB. Supports dietary restrictions, allergies, and Hinglish input/output!", examples=[ ["mujhe chawal aur dal hai, main kya bana sakta hoon jo gluten-free ho?", 600, 0.3, 0.95, True, False, "", 'hi'], ["Suggest a vegetarian dish with spinach and no nuts.", 600, 0.3, 0.95, False, False, "nuts", 'en'], ], cache_examples=False, ) if __name__ == "__main__": demo.launch()