app.py

import gradio as gr
from langchain_openai import ChatOpenAI
# from dspy import Agent  # Base class for custom agent
# from dspy import spawn_processes  # Distributed computing utility
from transformers import pipeline

# Choose model
model_name = "Dolphin-Phi"

# Load the chosen LLM model
llm = pipeline("text-generation", model=Dolphin-Phi)

# DSPy-based prompt generation
from dspy.agents import Agent
from dspy.utils import SentenceSplitter, SentimentAnalyzer, NamedEntityRecognizer

def dspy_generate_agent_prompts(prompt):
    """
    Generates prompts for different agents based on the provided prompt and DSPy functionalities.

    Args:
        prompt (str): The user-provided prompt (e.g., customer reviews).

    Returns:
        list: A list containing agent-specific prompts.
    """

    # 1. Split the prompt into individual sentences
    sentences = SentenceSplitter().process(prompt)

    # 2. Analyze sentiment for each sentence
    sentiment_analyzer = SentimentAnalyzer()
    sentiment_labels = []
    for sentence in sentences:
        sentiment_labels.append(sentiment_analyzer.analyze(sentence))

    # 3. Extract named entities related to specific topics
    ner = NamedEntityRecognizer(model_name="en_core_web_sm")
    extracted_entities = {}
    for sentence in sentences:
        entities = ner.process(sentence)
        for entity in entities:
            if entity.label_ in ["FOOD", "ORG", "LOCATION"]:  # Customize entity labels based on your needs
                extracted_entities.setdefault(entity.label_, []).append(entity.text)

    # 4. Craft prompts for each agent
    agent_prompts = []

    # **Sentiment Analyzer Prompt:**
    sentiment_prompt = f"Analyze the sentiment of the following sentences:\n" + "\n".join(sentences)
    agent_prompts.append(sentiment_prompt)

    # **Topic Extractor Prompt:** (Modify based on your specific topics)
    topic_prompt = f"Extract the main topics discussed in the following text, focusing on food, service, and ambiance:\n{prompt}"
    agent_prompts.append(topic_prompt)

    # **Recommendation Generator Prompt:** (Modify based on your requirements)
    positive_count = sum(label == "POSITIVE" for label in sentiment_labels)
    negative_count = sum(label == "NEGATIVE" for label in sentiment_labels)
    neutral_count = sum(label == "NEUTRAL" for label in sentiment_labels)
    topic_mentions = "\n".join(f"{k}: {','.join(v)}" for k, v in extracted_entities.items())

    recommendation_prompt = f"""Based on the sentiment analysis (positive: {positive_count}, negative: {negative_count}, neutral: {neutral_count}) and extracted topics ({topic_mentions}), suggest recommendations for the restaurant to improve."""
    agent_prompts.append(recommendation_prompt)

    return agent_prompts

# Define the main function to be used with Gradio
def generate_outputs(user_prompt):
    # 1. Process prompt with langchain (replace with your actual implementation)
    processed_prompt = langchain_function(user_prompt)  # Replace with your langchain logic

    # 2. Generate synthetic data using DSPy's distributed computing capabilities
    synthetic_data = generate_synthetic_data_distributed(processed_prompt)

    # 3. Combine user prompt and synthetic data
    combined_data = f"{user_prompt}\n{synthetic_data}"

    # 4. Generate prompts for agents using DSPy
    agent_prompts = dspy_generate_agent_prompts(processed_prompt)

    # 5. Use the chosen LLM for two of the prompts
    output_1 = llm(agent_prompts[0], max_length=100)[0]["generated_text"]
    output_2 = llm(agent_prompts[1], max_length=100)[0]["generated_text"]

    # 6. Produce outputs with Langchain or DSPy (replace with your actual implementation)
    report, recommendations, visualization = produce_outputs(combined_data)

    return report, recommendations, visualization

# Create the Gradio interface
gr.Interface(
    fn=generate_outputs,
    inputs=gr.Textbox(label="Enter a prompt"),
    outputs=["textbox", "textbox", "image"],
    title="Multi-Agent Prompt Processor",
    description="Processes a prompt using Langchain, DSPy, and a chosen Hugging Face LLM to generate diverse outputs.",
).launch()