Spaces:
Sleeping
Sleeping
Update app.py
Browse files
app.py
CHANGED
@@ -80,29 +80,6 @@ elif input_option == "Upload CSV File":
|
|
80 |
except Exception as e:
|
81 |
st.error(f"Error loading file: {e}")
|
82 |
|
83 |
-
def save_as_txt(content, filename):
|
84 |
-
with open(filename, "w") as f:
|
85 |
-
f.write(content)
|
86 |
-
return filename
|
87 |
-
|
88 |
-
def save_as_pdf(content, filename):
|
89 |
-
from fpdf import FPDF
|
90 |
-
pdf = FPDF()
|
91 |
-
pdf.add_page()
|
92 |
-
pdf.set_font("Arial", size=12)
|
93 |
-
for line in content.split('\n'):
|
94 |
-
pdf.multi_cell(0, 10, line)
|
95 |
-
pdf.output(filename)
|
96 |
-
return filename
|
97 |
-
|
98 |
-
# Show Dataset Preview Only After Loading
|
99 |
-
if st.session_state.df is not None and st.session_state.show_preview:
|
100 |
-
st.subheader("π Dataset Preview")
|
101 |
-
st.dataframe(st.session_state.df.head())
|
102 |
-
|
103 |
-
import tempfile
|
104 |
-
from fpdf import FPDF
|
105 |
-
|
106 |
# Helper Functions for Download
|
107 |
def save_as_txt(content, filename):
|
108 |
with open(filename, "w") as f:
|
@@ -171,7 +148,7 @@ if st.session_state.df is not None:
|
|
171 |
|
172 |
conclusion_writer = Agent(
|
173 |
role="Conclusion Specialist",
|
174 |
-
goal="Summarize findings into a clear and concise 3-5 line Conclusion highlighting only the most important
|
175 |
backstory="An expert in crafting impactful and clear conclusions.",
|
176 |
llm=llm,
|
177 |
)
|
@@ -227,26 +204,27 @@ if st.session_state.df is not None:
|
|
227 |
query = st.text_area("Enter Query:", value="Provide insights into the salary of a Principal Data Scientist.")
|
228 |
if st.button("Submit Query"):
|
229 |
with st.spinner("Processing query..."):
|
230 |
-
|
231 |
-
|
232 |
|
233 |
-
|
234 |
-
|
|
|
235 |
|
236 |
-
st.markdown(
|
237 |
|
238 |
-
|
239 |
-
|
240 |
-
|
241 |
-
|
242 |
-
|
243 |
|
|
|
244 |
fig_salary = px.box(st.session_state.df, x="job_title", y="salary_in_usd", title="Salary Distribution by Job Title")
|
245 |
st.plotly_chart(fig_salary)
|
246 |
st.caption("π Salary distribution across different job titles.")
|
247 |
|
248 |
-
fig_experience = px.bar(st.session_state.df.groupby("experience_level")["salary_in_usd"].mean().reset_index(),
|
249 |
-
x="experience_level", y="salary_in_usd", title="Average Salary by Experience Level")
|
250 |
st.plotly_chart(fig_experience)
|
251 |
st.caption("π Average salary based on experience level.")
|
252 |
|
@@ -254,7 +232,7 @@ if st.session_state.df is not None:
|
|
254 |
st.plotly_chart(fig_employment)
|
255 |
st.caption("π Salary distribution across employment types.")
|
256 |
|
257 |
-
st.markdown(
|
258 |
|
259 |
# Full Data Visualization Tab
|
260 |
with tab2:
|
@@ -271,15 +249,12 @@ if st.session_state.df is not None:
|
|
271 |
|
272 |
fig3 = px.box(st.session_state.df, x="employment_type", y="salary_in_usd", title="Salary Distribution by Employment Type")
|
273 |
st.plotly_chart(fig3)
|
274 |
-
st.caption("π Salary distribution
|
275 |
-
|
276 |
-
tab2_content = "Comprehensive Data Visualizations:\n"
|
277 |
-
tab2_content += "- Job Title Frequency\n"
|
278 |
-
tab2_content += "- Average Salary by Experience Level\n"
|
279 |
-
tab2_content += "- Salary Distribution by Employment Type\n"
|
280 |
|
|
|
281 |
tab2_txt = save_as_txt(tab2_content, "Tab2_Visualizations.txt")
|
282 |
tab2_pdf = save_as_pdf(tab2_content, "Tab2_Visualizations.pdf")
|
|
|
283 |
st.download_button("Download Tab 2 Summary as TXT", open(tab2_txt, "rb"), file_name="Tab2_Visualizations.txt")
|
284 |
st.download_button("Download Tab 2 Summary as PDF", open(tab2_pdf, "rb"), file_name="Tab2_Visualizations.pdf")
|
285 |
|
|
|
80 |
except Exception as e:
|
81 |
st.error(f"Error loading file: {e}")
|
82 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
83 |
# Helper Functions for Download
|
84 |
def save_as_txt(content, filename):
|
85 |
with open(filename, "w") as f:
|
|
|
148 |
|
149 |
conclusion_writer = Agent(
|
150 |
role="Conclusion Specialist",
|
151 |
+
goal="Summarize findings into a clear and concise 3-5 line Conclusion highlighting only the most important insights.",
|
152 |
backstory="An expert in crafting impactful and clear conclusions.",
|
153 |
llm=llm,
|
154 |
)
|
|
|
204 |
query = st.text_area("Enter Query:", value="Provide insights into the salary of a Principal Data Scientist.")
|
205 |
if st.button("Submit Query"):
|
206 |
with st.spinner("Processing query..."):
|
207 |
+
report_result = crew_report.kickoff(inputs={"query": query + " Provide detailed analysis but DO NOT include Conclusion."})
|
208 |
+
conclusion_result = crew_conclusion.kickoff(inputs={"query": query + " Provide ONLY the most important insights in 3-5 concise lines."})
|
209 |
|
210 |
+
# Convert results to string
|
211 |
+
report_text = str(report_result)
|
212 |
+
conclusion_text = str(conclusion_result)
|
213 |
|
214 |
+
st.markdown(report_text if report_text else "β οΈ No Report Generated.")
|
215 |
|
216 |
+
# Download Buttons for Tab 1
|
217 |
+
tab1_txt = save_as_txt(report_text, "Tab1_Report.txt")
|
218 |
+
tab1_pdf = save_as_pdf(report_text, "Tab1_Report.pdf")
|
219 |
+
st.download_button("Download Tab 1 Report as TXT", open(tab1_txt, "rb"), file_name="Tab1_Report.txt")
|
220 |
+
st.download_button("Download Tab 1 Report as PDF", open(tab1_pdf, "rb"), file_name="Tab1_Report.pdf")
|
221 |
|
222 |
+
# Visualizations with captions
|
223 |
fig_salary = px.box(st.session_state.df, x="job_title", y="salary_in_usd", title="Salary Distribution by Job Title")
|
224 |
st.plotly_chart(fig_salary)
|
225 |
st.caption("π Salary distribution across different job titles.")
|
226 |
|
227 |
+
fig_experience = px.bar(st.session_state.df.groupby("experience_level")["salary_in_usd"].mean().reset_index(), x="experience_level", y="salary_in_usd", title="Average Salary by Experience Level")
|
|
|
228 |
st.plotly_chart(fig_experience)
|
229 |
st.caption("π Average salary based on experience level.")
|
230 |
|
|
|
232 |
st.plotly_chart(fig_employment)
|
233 |
st.caption("π Salary distribution across employment types.")
|
234 |
|
235 |
+
st.markdown(conclusion_text if conclusion_text else "β οΈ No Conclusion Generated.")
|
236 |
|
237 |
# Full Data Visualization Tab
|
238 |
with tab2:
|
|
|
249 |
|
250 |
fig3 = px.box(st.session_state.df, x="employment_type", y="salary_in_usd", title="Salary Distribution by Employment Type")
|
251 |
st.plotly_chart(fig3)
|
252 |
+
st.caption("π Salary distribution across employment types.")
|
|
|
|
|
|
|
|
|
|
|
253 |
|
254 |
+
tab2_content = "Comprehensive Data Visualizations:\n- Job Title Frequency\n- Average Salary by Experience Level\n- Salary Distribution by Employment Type\n"
|
255 |
tab2_txt = save_as_txt(tab2_content, "Tab2_Visualizations.txt")
|
256 |
tab2_pdf = save_as_pdf(tab2_content, "Tab2_Visualizations.pdf")
|
257 |
+
|
258 |
st.download_button("Download Tab 2 Summary as TXT", open(tab2_txt, "rb"), file_name="Tab2_Visualizations.txt")
|
259 |
st.download_button("Download Tab 2 Summary as PDF", open(tab2_pdf, "rb"), file_name="Tab2_Visualizations.pdf")
|
260 |
|