Update app.py

app.py

@@ -1,7 +1,6 @@
 import streamlit as st
 import requests
 import openai
-import json
 from io import BytesIO
 from PIL import Image
 
@@ -49,9 +48,20 @@ st.markdown("""
 """, unsafe_allow_html=True)
 
 # Initialize session state variables
-for key in ["real_data", "story", "music_bytes", "image_bytes", "ngos", "points", "daily_challenges"]:
-    if key not in st.session_state:
-        st.session_state[key] = [] if key == "ngos" or key == "daily_challenges" else None
+if "real_data" not in st.session_state:
+    st.session_state.real_data = {}
+if "story" not in st.session_state:
+    st.session_state.story = ""
+if "music_bytes" not in st.session_state:
+    st.session_state.music_bytes = None
+if "image_bytes" not in st.session_state:
+    st.session_state.image_bytes = None
+if "ngos" not in st.session_state:
+    st.session_state.ngos = []
+if "points" not in st.session_state:
+    st.session_state.points = 0
+if "daily_challenges" not in st.session_state:
+    st.session_state.daily_challenges = []
 
 # Function to generate daily eco-friendly challenges
 def generate_daily_challenges() -> list:
@@ -131,20 +141,20 @@ def generate_image(description: str) -> bytes:
     return response.content
 
 # Function to fetch endangered species data
-def fetch_endangered_species(city: str) -> list:
-    prompt = f"List endangered species in {city} with their names, populations, and descriptions in JSON format."
+def fetch_endangered_species(city: str) -> dict:
+    prompt = f"Provide details of an endangered species in {city}, including its name, image description, and current population."
     response = openai.ChatCompletion.create(
         model="gpt-3.5-turbo",
         messages=[{"role": "user", "content": prompt}],
-        max_tokens=200,
-        temperature=0.7
+        max_tokens=150,
+        temperature=0.8
     )
     response_content = response.choices[0].message['content'].strip()
     try:
-        return json.loads(response_content)
-    except json.JSONDecodeError as e:
+        return eval(response_content)  # Assuming the response is a JSON-like structure
+    except Exception as e:
         st.error(f"Error parsing endangered species data: {e}")
-        return []
+        return {}
 
 # Function to fetch NGOs using OpenAI
 def fetch_nearby_ngos_with_openai(city: str, interests: list) -> list:
@@ -161,8 +171,13 @@ def fetch_nearby_ngos_with_openai(city: str, interests: list) -> list:
     response_content = response.choices[0].message['content'].strip()
     
     try:
-        return json.loads(response_content)
-    except json.JSONDecodeError as e:
+        ngo_list = eval(response_content)
+        if isinstance(ngo_list, list) and all(isinstance(ngo, dict) for ngo in ngo_list):
+            return ngo_list
+        else:
+            st.error("Unexpected response format. Could not parse NGO data.")
+            return []
+    except Exception as e:
         st.error(f"Error fetching NGO data: {e}")
         return []
 
@@ -175,6 +190,7 @@ city = st.text_input("Enter City Name:", placeholder="Type the name of a city...
 if st.button("Generate Environmental Data, Music, and Image"):
     st.session_state.real_data = fetch_real_data(city)
     if st.session_state.real_data:
+        # Generate narrative and mood
         narrative = create_narrative(city, st.session_state.real_data)
         mood = determine_mood(st.session_state.real_data)
         
@@ -225,11 +241,38 @@ for i, challenge in enumerate(st.session_state.daily_challenges):
 st.session_state.points = len(completed_challenges) * 10  # 10 points per challenge
 st.markdown(f"<h2 style='text-align: center;'>💰 Points: {st.session_state.points}</h2>", unsafe_allow_html=True)
 
-# Display the endangered species section after completing all challenges
+# Function to fetch endangered species data for a region
+def fetch_all_endangered_species(city: str) -> list:
+    prompt = (
+        f"Provide a list of endangered species found near {city}, "
+        "including their names, population estimates, and descriptions. "
+        "Return the data in JSON format as a list of dictionaries."
+    )
+    response = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo",
+        messages=[{"role": "user", "content": prompt}],
+        max_tokens=300,
+        temperature=0.8
+    )
+    response_content = response.choices[0].message['content'].strip()
+    
+    try:
+        species_list = eval(response_content)  # Assuming the response is a JSON-like structure
+        if isinstance(species_list, list) and all(isinstance(species, dict) for species in species_list):
+            return species_list
+        else:
+            st.error("Unexpected response format. Could not parse species data.")
+            return []
+    except Exception as e:
+        st.error(f"Error fetching endangered species data: {e}")
+        return []
+
+# Display the endangered species section
 if len(completed_challenges) == len(st.session_state.daily_challenges):
     st.success("All challenges completed! 🎉 You've unlocked the secret section!")
 
-    species_data_list = fetch_endangered_species(city)
+    # Fetch endangered species data for the user's city
+    species_data_list = fetch_all_endangered_species(city)
     if species_data_list:
         st.subheader("🦋 Endangered Species in Your Region")
         
@@ -268,4 +311,4 @@ if st.session_state.ngos:
         st.write(f"**{ngo.get('name', 'Unknown NGO')}**")
         st.write(f"📍 Location: {ngo.get('location', 'Unknown Location')}")
         st.write(f"🌱 Focus Area: {ngo.get('focus', 'Unknown Focus Area')}")
-        st.write("---")
+        st.write("---")