Update app.py

app.py

@@ -1,3 +1,24 @@
+import torch
+import gradio as gr
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import Chroma
+import openai
+import time
+import logging
+from datasets import load_dataset
+from nltk.tokenize import sent_tokenize
+import nltk
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Initialize OpenAI API key
+openai.api_key = 'YOUR_API_KEY'  # Replace with your API key
+
+# Download NLTK data
+nltk.download('punkt')
+
 # Initialize models and configurations
 model_name = 'intfloat/e5-small'
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -9,3 +30,91 @@ vectordb = Chroma(
     persist_directory='./docs/chroma/',
     embedding_function=embedding_model
 )
+
+def process_query(query):
+    try:
+        logger.info(f"Processing query: {query}")
+        
+        # Get relevant documents
+        relevant_docs = vectordb.similarity_search(query, k=30)
+        context = " ".join([doc.page_content for doc in relevant_docs])
+        
+        # Add delay to respect API rate limits
+        time.sleep(1)
+        
+        # Generate response using OpenAI
+        response = openai.chat.completions.create(
+            model="gpt-4",
+            messages=[
+                {"role": "system", "content": "You are a helpful assistant."},
+                {"role": "user", "content": f"Given the document: {context}\n\nGenerate a response to the query: {query}"}
+            ],
+            max_tokens=300,
+            temperature=0.7,
+        )
+        
+        answer = response.choices[0].message.content.strip()
+        logger.info("Successfully generated response")
+        
+        # Extract and display metrics
+        metrics = extract_metrics(query, answer, relevant_docs)
+        
+        return answer, metrics
+        
+    except Exception as e:
+        logger.error(f"Error processing query: {str(e)}")
+        return f"Error: {str(e)}", "Metrics unavailable"
+
+def extract_metrics(query, response, relevant_docs):
+    try:
+        context = " ".join([doc.page_content for doc in relevant_docs])
+        metrics_prompt = f"""
+        Question: {query}
+        Context: {context}
+        Response: {response}
+        
+        Extract metrics for:
+        - Context Relevance
+        - Context Utilization
+        - Completeness
+        - Response Quality
+        """
+        
+        metrics_response = openai.chat.completions.create(
+            model="gpt-4",
+            messages=[{"role": "user", "content": metrics_prompt}],
+            max_tokens=150,
+            temperature=0.7,
+        )
+        
+        return metrics_response.choices[0].message.content.strip()
+    except Exception as e:
+        return "Metrics calculation failed"
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=process_query,
+    inputs=[
+        gr.Textbox(
+            label="Enter your question",
+            placeholder="Type your question here...",
+            lines=2
+        )
+    ],
+    outputs=[
+        gr.Textbox(label="Answer", lines=5),
+        gr.Textbox(label="Metrics", lines=4)
+    ],
+    title="RAG-Powered Question Answering System",
+    description="Ask questions and get answers based on the embedded document knowledge.",
+    examples=[
+        ["What role does T-cell count play in severe human adenovirus type 55 (HAdV-55) infection?"],
+        ["In what school district is Governor John R. Rogers High School located?"],
+        ["Is there a functional neural correlate of individual differences in cardiovascular reactivity?"],
+        ["How do I select Natural mode?"]
+    ]
+)
+
+# Launch with debugging enabled
+if __name__ == "__main__":
+    demo.launch(debug=True)