Update app.py

app.py

@@ -5,18 +5,20 @@ from langchain_openai import ChatOpenAI
 from transformers import pipeline
 
 # Choose model
-model_name = "Dolphin-Phi"
+model_name = "dolphin-phi"
 
 # Load the chosen LLM model
-llm = pipeline("text-generation", model=Dolphin-Phi)
+llm = pipeline("text-generation", model=dolphin-phi)
 
 #Vectara config:
 # customer_id =
 # corpus_id = 
 # api_key = 
+import requests
 
 # DSPy-based prompt generation
 from dspy.agents import Agent
+from dspy import spawn_processes
 from dspy.utils import SentenceSplitter, SentimentAnalyzer, NamedEntityRecognizer
 
 def dspy_generate_agent_prompts(prompt):
@@ -177,10 +179,10 @@ def query_vectara(text):
 # 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
+    # processed_prompt = dspy_generate_agent_prompts(user_prompt)  # Replaced langchain logic with DSPy function below
 
     # 2. Generate synthetic data using DSPy's distributed computing capabilities
-    synthetic_data = generate_synthetic_data_distributed(processed_prompt)
+    synthetic_data = generate_synthetic_data_distributed(user_prompt)
 
     # 3. Combine user prompt and synthetic data
     combined_data = f"{user_prompt}\n{synthetic_data}"