ogegadavis254
commited on
Commit
•
c438b94
1
Parent(s):
c1cf6d8
Update app.py
Browse files
app.py
CHANGED
@@ -38,7 +38,8 @@ def call_ai_model(all_message):
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def get_performance_data(conditions):
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all_message = (
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f"Provide the expected sports performance score at conditions: "
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f"Temperature {conditions['temperature']}°C
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)
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response = call_ai_model(all_message)
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generated_text = ""
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@@ -57,33 +58,38 @@ def get_performance_data(conditions):
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continue
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# Example: Replace with actual data from API
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-
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return
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# Streamlit app layout
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st.title("Climate Impact on Sports Performance")
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st.write("Analyze and visualize the impact of
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#
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temperature = st.number_input("Temperature (°C):", min_value=-50, max_value=50, value=25)
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# Button to generate predictions
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if st.button("Generate Prediction"):
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conditions = {
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"temperature": temperature
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}
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try:
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with st.spinner("Generating predictions..."):
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# Call AI model to get qualitative analysis
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qualitative_analysis = (
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f"Assess the impact on sports performance at
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f"{temperature}°C
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)
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qualitative_result = call_ai_model(qualitative_analysis)
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# Get performance score for specified conditions
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st.success("Predictions generated.")
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@@ -93,17 +99,21 @@ if st.button("Generate Prediction"):
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# Display performance score
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st.subheader("Performance Score")
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st.write(f"Predicted Performance
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# Plotting the data
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st.subheader("Performance Score vs
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#
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fig, ax = plt.subplots()
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ax.plot(
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ax.set_xlabel('
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ax.set_ylabel('
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ax.set_title('Performance Score vs
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ax.grid(True)
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st.pyplot(fig)
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def get_performance_data(conditions):
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all_message = (
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f"Provide the expected sports performance score at conditions: "
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f"Temperature {conditions['temperature']}°C, Humidity {conditions['humidity']}%, "
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f"Wind Speed {conditions['wind_speed']} km/h."
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)
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response = call_ai_model(all_message)
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generated_text = ""
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continue
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# Example: Replace with actual data from API
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performance_score = 80 # Replace with actual data from API
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return performance_score
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# Streamlit app layout
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st.title("Climate Impact on Sports Performance")
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st.write("Analyze and visualize the impact of climate conditions on sports performance.")
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# Inputs for climate conditions
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temperature = st.number_input("Temperature (°C):", min_value=-50, max_value=50, value=25)
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humidity = st.number_input("Humidity (%):", min_value=0, max_value=100, value=50)
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wind_speed = st.number_input("Wind Speed (km/h):", min_value=0.0, max_value=200.0, value=15.0)
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# Button to generate predictions
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if st.button("Generate Prediction"):
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conditions = {
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"temperature": temperature,
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"humidity": humidity,
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"wind_speed": wind_speed
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}
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try:
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with st.spinner("Generating predictions..."):
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# Call AI model to get qualitative analysis
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qualitative_analysis = (
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f"Assess the impact on sports performance at conditions: "
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f"Temperature {temperature}°C, Humidity {humidity}%, "
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f"Wind Speed {wind_speed} km/h."
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)
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qualitative_result = call_ai_model(qualitative_analysis)
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# Get performance score for specified conditions
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performance_score = get_performance_data(conditions)
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st.success("Predictions generated.")
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# Display performance score
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st.subheader("Performance Score")
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st.write(f"Predicted Performance Score: {performance_score}")
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# Plotting the data
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st.subheader("Performance Score vs Climate Conditions")
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# Define climate conditions for plotting
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climate_conditions = list(conditions.keys())
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climate_values = list(conditions.values())
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# Plotting performance score against climate conditions
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fig, ax = plt.subplots()
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ax.plot(climate_conditions, climate_values, marker='o', linestyle='-', color='b')
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ax.set_xlabel('Climate Conditions')
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ax.set_ylabel('Value')
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ax.set_title('Performance Score vs Climate Conditions')
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ax.grid(True)
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st.pyplot(fig)
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