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app.py

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+import streamlit as st
+import requests
+import pandas as pd
+
+# --- API Configuration ---
+API_URL = "https://adrienbrdne-fastapi-kig.hf.space/"
+ENDPOINT = f"{API_URL}/generate-key-issues" # API endpoint name remains unchanged
+
+# --- Streamlit Interface ---
+st.set_page_config(page_title="Problematic Generator", layout="wide")
+st.title("Generate Problematics from Query")
+
+# Input field for the user query
+user_query = st.text_input(
+    "Enter your query here:",
+    placeholder="Example: deploying edge computing for real-time AI-driven traffic management systems in smart cities"
+)
+
+# Button to submit the query
+if st.button("Generate Problematics"):
+    if user_query:  # Check if the user entered something
+        st.info(f"Sending query to API: {ENDPOINT}")
+        st.write(f"Query: \"{user_query}\"")
+
+        # Prepare data for the POST request
+        data = {"query": user_query}
+
+        try:
+            # Show a loading spinner during the API call
+            with st.spinner("Calling API to generate problematics..."):
+                response = requests.post(ENDPOINT, json=data, timeout=60) # Added a timeout
+
+            # Check the response status
+            if response.status_code == 200:
+                st.success("Response received successfully!")
+                result = response.json()
+
+                # Check if 'key_issues' key exists and is a list
+                if 'key_issues' in result and isinstance(result['key_issues'], list):
+                    # Convert the 'key_issues' list to a Pandas DataFrame
+                    df = pd.DataFrame(result['key_issues'])
+
+                    # Remove the 'id' column if it exists
+                    if 'id' in df.columns:
+                        df_display = df.drop(columns=['id'])
+                    else:
+                        st.warning("Column 'id' was not found in the received data.")
+                        df_display = df
+
+                    # Display the DataFrame
+                    st.subheader("Generated Problematics (DataFrame):")
+                    st.dataframe(df_display, use_container_width=True) # Display dataframe with full container width
+
+                    # Optional: Display raw JSON response for verification
+                    # st.subheader("Raw JSON Response Received:")
+                    # st.json(result)
+
+                else:
+                    st.error("API response does not contain the expected 'key_issues' key or it is not a list.")
+                    st.json(result) # Display the received response for debugging
+
+            else:
+                # Display an error if status is not 200
+                st.error(f"API Call Error: Status {response.status_code}")
+                try:
+                    # Try to display the API error message if it's JSON
+                    error_details = response.json()
+                    st.json(error_details)
+                except requests.exceptions.JSONDecodeError:
+                    # Otherwise, display the raw text response
+                    st.text(response.text)
+
+        except requests.exceptions.RequestException as e:
+            # Handle connection or timeout errors
+            st.error(f"API Connection Error: {e}")
+        except Exception as e:
+            # Handle any other unexpected errors
+            st.error(f"An unexpected error occurred: {e}")
+
+    else:
+        # Message if the user clicks the button without entering a query
+        st.warning("Please enter a query before generating.")
+
+# Initial instructions
+else:
+    st.info("Enter a query in the field above and click 'Generate Problematics' to query the API.")

requirements.txt

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+streamlit
+requests
+pandas