trying out the LLM interface

app.py

@@ -12,14 +12,15 @@ model_reply_with_graph = pipeline('text2text-generation', model='google/flan-t5-
 def generate_input_graph(request_text):
     """
     Model 1: Convert the customer request into a structured graph.
-    Output only valid JSON with exactly two keys: 'nodes' and 'edges', with no additional text.
+    The prompt now includes an explicit example to guide the model.
     """
     prompt = (
         "You are an assistant that converts a natural language customer request into a structured graph. "
-        "Output only valid JSON with exactly two keys: 'nodes' and 'edges'. "
-        "'nodes' should be a non-empty list of unique strings representing node names. "
-        "'edges' should be a non-empty list of pairs [source, target] representing directed edges between nodes. "
-        "Do not include any extra text, commentary, or formatting.\n\n"
+        "Output only valid JSON with exactly two keys: 'nodes' and 'edges'.\n"
+        "Example output:\n"
+        '{"nodes": ["City A", "City B", "City C", "City D", "City E"], '
+        '"edges": [["City A", "City B"], ["City B", "City C"], ["City C", "City D"], ["City D", "City E"]]} \n\n'
+        "Do not include any extra text, explanation, or commentary.\n\n"
         f"Customer Request: \"{request_text}\"\n\n"
         "Structured Graph JSON:"
     )
@@ -29,13 +30,14 @@ def generate_input_graph(request_text):
 def generate_reply_and_graph(request_text):
     """
     Model 2: Generate a detailed response along with a structured graph.
-    First, provide a textual reply, then output a valid JSON graph with keys 'nodes' and 'edges' (no extra text).
+    The prompt now includes an explicit example of the expected JSON format.
     """
     prompt = (
-        "You are an assistant that responds to a customer request with a detailed and structured reply. "
-        "First, provide a helpful textual reply to the request. Then, output a valid JSON object "
-        "with exactly two keys: 'nodes' and 'edges'. 'nodes' should be a non-empty list of unique strings, "
-        "and 'edges' should be a non-empty list of pairs [source, target] representing directed edges. "
+        "You are an assistant that responds to a customer request with a detailed reply and a structured graph. "
+        "First, provide a helpful textual reply to the request. Then output a valid JSON object with exactly two keys: 'nodes' and 'edges'.\n"
+        "Example JSON output:\n"
+        '{"nodes": ["City A", "City B", "City C", "City D", "City E"], '
+        '"edges": [["City A", "City B"], ["City B", "City C"], ["City C", "City D"], ["City D", "City E"]]} \n\n'
         "Do not include any extra text or commentary outside the JSON.\n\n"
         f"Customer Request: \"{request_text}\"\n\n"
         "Detailed Response and Structured Graph JSON:"
@@ -48,6 +50,7 @@ def try_parse_json(text):
     Attempt to extract a valid JSON substring from the model output using a non-greedy match.
     Returns the parsed JSON if valid, otherwise logs the error and returns None.
     """
+    st.write("Raw model output:", text)
     # Non-greedy match for JSON object
     json_pattern = re.compile(r'\{.*?\}', re.DOTALL)
     match = json_pattern.search(text)
@@ -96,7 +99,7 @@ def main():
     st.title("Two-LLM Pipeline: Request & Reply with Graph Visualization")
     st.write(
         "Enter a customer request to see it transformed into an input graph and then receive a detailed reply "
-        "with its own structured graph. Both graphs are built using non-empty 'nodes' and 'edges' for transparent input and output."
+        "with its own structured graph. Both graphs are built using non-empty 'nodes' and 'edges'."
     )
     
     customer_request = st.text_area("Enter Customer Request:", height=150)
@@ -121,7 +124,6 @@ def main():
         # --- Stage 2: Detailed Reply with Structured Graph ---
         st.subheader("Stage 2: Detailed Response with Structured Graph")
         model2_output = generate_reply_and_graph(customer_request)
-        st.write("Detailed Response and Graph (Raw Output):")
         st.code(model2_output, language="json")
         output_graph_data = try_parse_json(model2_output)
         if output_graph_data:
@@ -133,5 +135,3 @@ def main():
 
 if __name__ == "__main__":
     main()
-
-