Update app.py

app.py

@@ -105,7 +105,7 @@ if st.button("Generate Prediction and 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:
+    try:
         stages = [
             "Analyzing climate conditions...",
             "Checking location data...",
@@ -143,131 +143,131 @@ if st.button("Generate Prediction and Analysis"):
                         continue
     
             st.success("Analysis completed!")
-        # Display prediction
-        st.subheader("Climate Impact Analysis for Sports in Kenya")
-        st.markdown(initial_text.strip())
+            # 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()
+            # 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)
+            # 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)
+            # 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)
+            # 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)
+            # 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}")
+            # 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. and make the response very precise and short, do not yap"
-        )
+            # 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. and make the response very precise and short, do not yap"
+            )
         
-        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:]
-                    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
+            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:]
+                        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())
+            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. And make the response very precise and short, do not yap"
-        )
+            # 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. And make the response very precise and short, do not yap"
+            )
         
-        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:
-                                mitigation_text += delta["content"]
-                    except json.JSONDecodeError:
-                        continue
+            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:
+                                    mitigation_text += delta["content"]
+                        except json.JSONDecodeError:
+                            continue
         
-        st.markdown(mitigation_text.strip())
+            st.markdown(mitigation_text.strip())
 
-        # Display raw analysis result for debugging
-        with st.expander("Show Raw Analysis"):
-            st.text(initial_text)
+            # 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}")
     except requests.exceptions.RequestException as re:
         st.error(f"Request error: {re}")
     except Exception as e:
-        st.error(f"An unexpected error occurred: {e}")
+        st.error(f"An unexpected error occurred: {e}")