Update app.py

app.py

@@ -64,8 +64,8 @@ def call_ai_model_analysis(analysis_text):
     return response
 
 # Streamlit app layout
-st.title("Climate Impact on Sports Performance and Infrastructure")
-st.write("Analyze and visualize the impact of climate conditions on sports performance and infrastructure.")
+st.title("Climate Impact on Sports Performance and Infrastructure in Kenya")
+st.write("Analyze and visualize the impact of climate conditions on sports performance and infrastructure, with a focus on Kenya.")
 
 # Inputs for climate conditions
 temperature = st.number_input("Temperature (°C):", min_value=-50, max_value=50, value=25)
@@ -76,27 +76,37 @@ air_quality_index = st.number_input("Air Quality Index:", min_value=0, max_value
 precipitation = st.number_input("Precipitation (mm):", min_value=0.0, max_value=500.0, value=10.0)
 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"])
+elevation = st.number_input("Elevation (m):", min_value=0, max_value=5000, value=1000)
+
 # Sports and athlete inputs
-sports = st.multiselect("Select sports:", ["Football", "Tennis", "Athletics", "Swimming", "Basketball", "Golf"])
+sports = st.multiselect("Select sports:", ["Athletics", "Football", "Rugby", "Volleyball", "Boxing", "Swimming"])
 athlete_types = st.multiselect("Select athlete types:", ["Professional", "Amateur", "Youth", "Senior"])
 
 # Infrastructure inputs
-infrastructure_types = st.multiselect("Select infrastructure types:", ["Outdoor Stadium", "Indoor Arena", "Swimming Pool", "Tennis Court", "Golf Course"])
+infrastructure_types = st.multiselect("Select infrastructure types:", ["Outdoor Stadium", "Indoor Arena", "Training Facility", "Community Sports Ground"])
 
-if st.button("Generate Prediction"):
+if st.button("Generate Prediction and Analysis"):
     all_message = (
-        f"Assess the impact on sports performance, athletes, and infrastructure based on climate conditions: "
+        f"Assess the impact on sports performance, athletes, and infrastructure in Kenya based on climate conditions: "
         f"Temperature {temperature}°C, Humidity {humidity}%, Wind Speed {wind_speed} km/h, UV Index {uv_index}, "
         f"Air Quality Index {air_quality_index}, Precipitation {precipitation} mm, Atmospheric Pressure {atmospheric_pressure} hPa. "
+        f"Region: {region}, Elevation: {elevation}m. "
         f"Sports: {', '.join(sports)}. Athlete types: {', '.join(athlete_types)}. "
         f"Infrastructure types: {', '.join(infrastructure_types)}. "
-        f"Provide a detailed analysis of how these conditions affect performance, health, and infrastructure. "
+        f"Provide a detailed analysis of how these conditions affect performance, health, and infrastructure in Kenya. "
         f"Include specific impacts for each sport, athlete type, and infrastructure type. "
-        f"Also, provide an overall performance score and an infrastructure impact score, both as percentages. Lastly i need you organize everything in tables, not random paragraphs, use the following columns: Climate Conditions,	Impact on Sports Performance,	Impact on Athletes' Health,	Impact on Infrastructure and do your best to be accurate in your analysis."
+        f"Also, provide an overall performance score and an infrastructure impact score, both as percentages. "
+        f"Suggest mitigation strategies for both performance and infrastructure. "
+        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."
     )
 
     try:
-        with st.spinner("Analyzing climate conditions..."):
+        with st.spinner("Analyzing climate conditions and generating predictions..."):
             initial_response = call_ai_model_initial(all_message)
 
             initial_text = ""
@@ -114,97 +124,129 @@ if st.button("Generate Prediction"):
                     except json.JSONDecodeError:
                         continue
 
-            st.success("Initial analysis completed!")
+            st.success("Analysis completed!")
+
+        # Display prediction
+        st.subheader("Climate Impact Analysis for Sports in Kenya")
+        st.markdown(initial_text.strip())
+
+        # Extract and display scores
+        performance_score = "N/A"
+        infrastructure_score = "N/A"
+        for line in initial_text.split('\n'):
+            if "performance score:" in line.lower():
+                performance_score = line.split(":")[-1].strip()
+            elif "infrastructure impact score:" in line.lower():
+                infrastructure_score = line.split(":")[-1].strip()
+
+        # Display performance and infrastructure scores
+        col1, col2 = st.columns(2)
+        with col1:
+            st.metric("Overall Performance Score", performance_score)
+        with col2:
+            st.metric("Infrastructure Impact Score", infrastructure_score)
+
+        # Prepare data for visualization
+        results_data = {
+            "Condition": ["Temperature", "Humidity", "Wind Speed", "UV Index", "Air Quality Index", "Precipitation", "Atmospheric Pressure"],
+            "Value": [temperature, humidity, wind_speed, uv_index, air_quality_index, precipitation, atmospheric_pressure]
+        }
+        results_df = pd.DataFrame(results_data)
 
-        with st.spinner("Generating predictions..."):
-            analysis_text = (
-                f"Based on the following analysis, provide a performance score and an infrastructure impact score, "
-                f"both as percentages. Include lines that say 'Performance Score: XX%' and 'Infrastructure Impact Score: YY%' "
-                f"in your response. Here's the text to analyze: {initial_text}"
-            )
-            analysis_response = call_ai_model_analysis(analysis_text)
+        # Display results in a table
+        st.subheader("Climate Conditions Summary")
+        st.table(results_df)
 
-            analysis_result = ""
-            for line in analysis_response.iter_lines():
+        # Create a radar chart for climate conditions
+        fig = go.Figure(data=go.Scatterpolar(
+            r=[temperature/50*100, humidity, wind_speed/2, uv_index/11*100, air_quality_index/5, precipitation/5, (atmospheric_pressure-900)/2],
+            theta=results_df['Condition'],
+            fill='toself'
+        ))
+        fig.update_layout(
+            polar=dict(
+                radialaxis=dict(visible=True, range=[0, 100])
+            ),
+            showlegend=False
+        )
+        st.plotly_chart(fig)
+
+        # Display analyzed sports and infrastructure
+        st.subheader("Analyzed Components")
+        col1, col2, col3 = st.columns(3)
+        with col1:
+            st.write("**Sports:**")
+            for sport in sports:
+                st.write(f"- {sport}")
+        with col2:
+            st.write("**Athlete Types:**")
+            for athlete_type in athlete_types:
+                st.write(f"- {athlete_type}")
+        with col3:
+            st.write("**Infrastructure Types:**")
+            for infra_type in infrastructure_types:
+                st.write(f"- {infra_type}")
+
+        # Socio-economic impact analysis
+        st.subheader("Socio-Economic Impact Analysis")
+        socio_economic_prompt = (
+            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."
+        )
+        
+        with st.spinner("Analyzing socio-economic impacts..."):
+            socio_economic_response = call_ai_model_analysis(socio_economic_prompt)
+            socio_economic_text = ""
+            for line in socio_economic_response.iter_lines():
                 if line:
                     line_content = line.decode('utf-8')
                     if line_content.startswith("data: "):
-                        line_content = line_content[6:]  # Strip "data: " prefix
+                        line_content = line_content[6:]
+                    try:
+                        json_data = json.loads(line_content)
+                        if "choices" in json_data:
+                            delta = json_data["choices"][0]["delta"]
+                            if "content" in delta:
+                                socio_economic_text += delta["content"]
+                    except json.JSONDecodeError:
+                        continue
+        
+        st.markdown(socio_economic_text.strip())
+
+        # Mitigation strategies
+        st.subheader("Mitigation Strategies")
+        mitigation_prompt = (
+            f"Based on the climate conditions and sports analysis for {region}, Kenya, "
+            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."
+        )
+        
+        with st.spinner("Generating mitigation strategies..."):
+            mitigation_response = call_ai_model_analysis(mitigation_prompt)
+            mitigation_text = ""
+            for line in mitigation_response.iter_lines():
+                if line:
+                    line_content = line.decode('utf-8')
+                    if line_content.startswith("data: "):
+                        line_content = line_content[6:]
                     try:
                         json_data = json.loads(line_content)
                         if "choices" in json_data:
                             delta = json_data["choices"][0]["delta"]
                             if "content" in delta:
-                                analysis_result += delta["content"]
+                                mitigation_text += delta["content"]
                     except json.JSONDecodeError:
                         continue
+        
+        st.markdown(mitigation_text.strip())
 
-            st.success("Predictions generated!")
-
-            # Extract performance and infrastructure scores from the analysis result
-            performance_score = "N/A"
-            infrastructure_score = "N/A"
-            for line in analysis_result.split('\n'):
-                if "performance score:" in line.lower():
-                    performance_score = line.split(":")[-1].strip()
-                elif "infrastructure impact score:" in line.lower():
-                    infrastructure_score = line.split(":")[-1].strip()
-
-            # Prepare data for visualization
-            results_data = {
-                "Condition": ["Temperature", "Humidity", "Wind Speed", "UV Index", "Air Quality Index", "Precipitation", "Atmospheric Pressure"],
-                "Value": [temperature, humidity, wind_speed, uv_index, air_quality_index, precipitation, atmospheric_pressure]
-            }
-            results_df = pd.DataFrame(results_data)
-
-            # Display results in a table
-            st.subheader("Climate Conditions Summary")
-            st.table(results_df)
-
-            # Create a radar chart for climate conditions
-            fig = go.Figure(data=go.Scatterpolar(
-                r=[temperature/50*100, humidity, wind_speed/2, uv_index/11*100, air_quality_index/5, precipitation/5, (atmospheric_pressure-900)/2],
-                theta=results_df['Condition'],
-                fill='toself'
-            ))
-            fig.update_layout(
-                polar=dict(
-                    radialaxis=dict(visible=True, range=[0, 100])
-                ),
-                showlegend=False
-            )
-            st.plotly_chart(fig)
-
-            # Display prediction
-            st.subheader("Predicted Impact on Performance and Infrastructure")
-            st.markdown(initial_text.strip())
-
-            # Display performance and infrastructure scores
-            col1, col2 = st.columns(2)
-            with col1:
-                st.metric("Performance Score", performance_score)
-            with col2:
-                st.metric("Infrastructure Impact Score", infrastructure_score)
-
-            # Display analyzed sports and infrastructure
-            st.subheader("Analyzed Components")
-            col1, col2, col3 = st.columns(3)
-            with col1:
-                st.write("**Sports:**")
-                for sport in sports:
-                    st.write(f"- {sport}")
-            with col2:
-                st.write("**Athlete Types:**")
-                for athlete_type in athlete_types:
-                    st.write(f"- {athlete_type}")
-            with col3:
-                st.write("**Infrastructure Types:**")
-                for infra_type in infrastructure_types:
-                    st.write(f"- {infra_type}")
-
-            # Display raw analysis result for debugging
-            with st.expander("Show Raw Analysis"):
-                st.text(analysis_result)
+        # Display raw analysis result for debugging
+        with st.expander("Show Raw Analysis"):
+            st.text(initial_text)
 
     except ValueError as ve:
         st.error(f"Configuration error: {ve}")