OVERHAUL

app.py

@@ -1,133 +1,98 @@
 import gradio as gr
-
-# This may be in the wrong spot
-def generate_outputs(user_prompt):
-    report, recommendations, visualization = produce_outputs(combined_data)
-    return report, recommendations, visualization
-
 from langchain_openai import TextProcessingAgent
 from dspy.agents import Agent  # Base class for custom agent
 from dspy.utils import spawn_processes  # Distributed computing utility
+from transformers import pipeline
 
-# API key **ADD A KEY OR LOCAL LLM PATHWAY**
-openai = OpenAI(api_key="KEY")
-
+# Choose model
+model_name = "Dolphin-Phi"
 
-# User prompt intake was here but was folded into Gradio interface lines
+# 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
 
-# Synthetic data generation (using Langchain's Text-Davinci-003 model for illustration)
-def generate_synthetic_data(prompt):
-    response = openai.complete(prompt=prompt, engine="text-davinci-003", max_tokens=100)
-    return response.choices[0].text
+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).
 
-# Custom data processing agent (inheriting from DSPy's Agent class)  [TONIC PLEASE HELP LOL]
-class DataProcessingAgent(Agent):
-    def __init__(self):
-        super().__init__()
+    Returns:
+        list: A list containing agent-specific prompts.
+    """
 
-    def process(self, data):
-        # Implement our custom data processing logic here (e.g., feature engineering)
-        processed_data = data.lower().strip()
-        return processed_data
+    # 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))
 
-# Dynamic team composition (replace with logic for dynamic team creation)
-team = [
-    OpenAI(api_key="YOUR_OPENAI_API_KEY", engine="text-davinci-003"),  # LLM agent
-    DataProcessingAgent(),  # Custom data processing agent
-]
+    # 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 = []
 
-# Prompt and data flow refinement
-message = f"{user_prompt}\n{generate_synthetic_data(f'Simulate scenarios for {user_prompt}')}"
+    # **Sentiment Analyzer Prompt:**
+    sentiment_prompt = f"Analyze the sentiment of the following sentences:\n" + "\n".join(sentences)
+    agent_prompts.append(sentiment_prompt)
 
-# Agents Group Chat Finite State Machine
-class GroupChatFSM:
-    def __init__(self, teams_config):
-        """
-        Initialize with configurations for teams.
-        """
-        self.teams = {team_name: self.Team(team_agents) for team_name, team_agents in teams_config.items()}
-        self.states = ["waiting", "interacting", "finalizing"]
-        self.current_state = "waiting"
+    # **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)
 
-    def transition(self, to_state):
-        """
-        Transition the state of the group chat based on FSM rules.
-        """
-        if to_state in self.states:
-            self.current_state = to_state
-        else:
-            raise ValueError("Invalid state transition attempted.")
+    # **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())
 
-    def broadcast(self, message):
-        """
-        Broadcast a message to all teams based on the current FSM state.
-        """
-        if self.current_state == "interacting":
-            responses = {team_name: team.broadcast(message) for team_name, team in self.teams.items()}
-            return responses
-        else:
-            return "The group chat is not in an interacting state."
+    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)
 
-    class Team:
-        def __init__(self, agents_config):
-            self.agents = [self.Agent(agent_config) for agent_config in agents_config]
+    return agent_prompts
 
-        def broadcast(self, message):
-            responses = [agent.respond(message) for agent in self.agents]
-            return responses
-
-        class Agent:
-            def __init__(self, config):
-                self.agent_name = config['agent_name']
-                self.api_key = config['api_key']
-                self.model = config['model']
-
-            def respond(self, message):
-                return f"{self.agent_name} responding with {self.model}"
+# 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
 
-# Multimedia output production (using Langchain's Text-Davinci-003 as default) because I don't know how to implement DSPy properly yet  [TONIC PLEASE HELP LOL]
-def produce_outputs(processed_data):
-    # Use Langchain for LLM-based analysis, recommendations, etc. Should this be updated to DSPy too? again:[TONIC PLEASE HELP LOL]
-    analysis = openai.complete(prompt=f"Analyze {processed_data}", engine="text-davinci-003", max_tokens=200)
-    recommendations = openai.complete(prompt=f"Recommend strategies based on {processed_data}", engine="text-davinci-003", max_tokens=100)
-    # Replace with your visualization logic
-    visualization = None
-    return analysis.choices[0].text, recommendations.choices[0].text, visualization
+    # 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}"
 
-# Synth data generation using DSPy's distributed computing capabilities (taken partially from DSPY documentation)
-def generate_synthetic_data_distributed(prompt, num_nodes=3):
-    # Spawn synthetic data generation processes across multiple nodes
-    processes = [spawn_processes(generate_synthetic_data, [f"Simulate scenarios for {prompt}"]) for _ in range(num_nodes)]
+    # 4. Generate prompts for agents using DSPy
+    agent_prompts = dspy_generate_agent_prompts(processed_prompt)
 
-    # Collect the results from each node
-    synthetic_data_list = []
-    for process in processes:
-        synthetic_data_list.extend(process.get())
+    # 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"]
 
-    # Combine the results and return the synthetic data
-    return "\n".join(synthetic_data_list)
+    # 6. Produce outputs with Langchain or DSPy (replace with your actual implementation)
+    report, recommendations, visualization = produce_outputs(combined_data)
 
+    return report, recommendations, visualization
 
-# Generate synthetic data using DSPy's distributed computing capabilities. Again:[TONIC PLEASE HELP LOL]
-synthetic_data = generate_synthetic_data_distributed(user_prompt)
-
-
-# Generate outputs
-report, recommendations, visualization = produce_outputs(combined_data)
-
-
-# Print the results for testing pyrposes
-print("Report:")
-print(report)
-print("\nRecommendations:")
-print(recommendations)
-print("\nVisualization:")
-print(visualization)  # Currently "None" due to placeholder 'visualization'
-
-# Moved from other gradio chunk near the top
-gr.Interface(fn=generate_outputs, inputs=user_prompt, outputs=["text", "text", "image"]).launch()
+# 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()