Update app.py

app.py

@@ -77,7 +77,7 @@ precipitation = st.number_input("Precipitation (mm):", min_value=0.0, max_value=
 atmospheric_pressure = st.number_input("Atmospheric Pressure (hPa):", min_value=900, max_value=1100, value=1013)
 
 # Kenya-specific inputs
-region = st.selectbox("Select region in Kenya:", ["Nairobi", "Mombasa", "Kisumu", "Nakuru", "Eldoret"])
+region = st.text_input("Enter region in Kenya:")
 elevation = st.number_input("Elevation (m):", min_value=0, max_value=5000, value=1000)
 
 # Sports and athlete inputs
@@ -102,30 +102,47 @@ if st.button("Generate Prediction and Analysis"):
         f"Assess the socio-economic implications of these climate impacts on sports in Kenya, including equitable access to sports facilities. "
         f"Organize the information in tables with the following columns: Climate Conditions, Impact on Sports Performance, "
         f"Impact on Athletes' Health, Impact on Infrastructure, Mitigation Strategies, Socio-Economic Implications. "
-        f"Be as accurate and specific to Kenya as possible in your analysis."
+        f"Be as accurate and specific to Kenya as possible in your analysis. And please do not generate long texts, make it as short and precise as possible, i am stressing on this please, generate something short."
     )
 
-    try:
-        with st.spinner("Analyzing climate conditions and generating predictions..."):
-            initial_response = call_ai_model_initial(all_message)
-
-            initial_text = ""
-            for line in initial_response.iter_lines():
-                if line:
-                    line_content = line.decode('utf-8')
-                    if line_content.startswith("data: "):
-                        line_content = line_content[6:]  # Strip "data: " prefix
-                    try:
-                        json_data = json.loads(line_content)
-                        if "choices" in json_data:
-                            delta = json_data["choices"][0]["delta"]
-                            if "content" in delta:
-                                initial_text += delta["content"]
-                    except json.JSONDecodeError:
-                        continue
+   try:
+    stages = [
+        "Analyzing climate conditions...",
+        "Checking location data...",
+        "Fetching historical data...",
+        "Running simulations...",
+        "Processing current weather...",
+        "Assessing environmental factors...",
+        "Calculating predictions...",
+        "Compiling results...",
+        "Finalizing analysis...",
+        "Preparing output..."
+    ]
+    
+    with st.spinner("Analyzing climate conditions and generating predictions..."):
+        # Loop through each stage, updating the spinner text and waiting for 2 seconds
+        for stage in stages:
+            st.spinner(stage)
+            time.sleep(2)
+        
+        initial_response = call_ai_model_initial(all_message)
 
-            st.success("Analysis completed!")
+        initial_text = ""
+        for line in initial_response.iter_lines():
+            if line:
+                line_content = line.decode('utf-8')
+                if line_content.startswith("data: "):
+                    line_content = line_content[6:]  # Strip "data: " prefix
+                try:
+                    json_data = json.loads(line_content)
+                    if "choices" in json_data:
+                        delta = json_data["choices"][0]["delta"]
+                        if "content" in delta:
+                            initial_text += delta["content"]
+                except json.JSONDecodeError:
+                    continue
 
+        st.success("Analysis completed!")
         # Display prediction
         st.subheader("Climate Impact Analysis for Sports in Kenya")
         st.markdown(initial_text.strip())
@@ -193,7 +210,7 @@ if st.button("Generate Prediction and Analysis"):
             f"Based on the climate conditions and sports analysis for {region}, Kenya, "
             f"provide a brief assessment of the socio-economic implications, including impacts on: "
             f"1) Local economy, 2) Community health, 3) Sports tourism, 4) Equitable access to sports facilities. "
-            f"Consider the specific context of Kenya and the selected region."
+            f"Consider the specific context of Kenya and the selected region. and make the response very precise and short, do not yap"
         )
         
         with st.spinner("Analyzing socio-economic impacts..."):
@@ -222,7 +239,7 @@ if st.button("Generate Prediction and Analysis"):
             f"suggest specific mitigation strategies for: "
             f"1) Improving athlete performance and health, 2) Enhancing infrastructure resilience, "
             f"3) Ensuring equitable access to sports facilities. "
-            f"Consider the specific context of Kenya and the selected region."
+            f"Consider the specific context of Kenya and the selected region. And make the response very precise and short, do not yap"
         )
         
         with st.spinner("Generating mitigation strategies..."):