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https://github.com/kennethreitz/instructor.git
synced 2026-06-05 22:50:18 +00:00
improve streamlit app
This commit is contained in:
Jason
@@ -25,7 +25,7 @@ class StreamingAccumulatorManager:
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try:
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# Replace this line with your validation logic
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obj = m.MultiSearch.model_validate(obj)
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self.update(index, obj)
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self.update(index, obj.model_dump())
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self.accumulator[Status.IS_VALID.value].update(index, True)
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except ValidationError as e:
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self.accumulator[Status.IS_VALID.value].update(index, False)
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@@ -59,8 +59,6 @@ class StreamingAccumulatorManager:
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elif isinstance(data, Enum):
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enum_path = f"{path}.enum"
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self.accumulator[enum_path].update(index, data.value)
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elif path != "$":
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pass
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else:
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self.accumulator[path].update(index, data)
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@@ -0,0 +1,224 @@
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from collections import Counter
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stats_dict = {
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"$.queries.length": {
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"_reverse_lookup": {
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1: [0, 1, 8, 9, 10, 13, 14, 15],
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2: [7, 11, 16],
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3: [12, 17],
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},
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"counter": Counter({1: 8, 2: 3, 3: 2}),
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"max": 3,
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"mean": 1.5384615384615385,
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"min": 1,
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"missing_values": 0,
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"std": 0.7457969011409735,
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"unique_count": 3,
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},
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"$.queries[*].is_priority": {
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"_reverse_lookup": {False: [13], True: [1, 9, 14, 17]},
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"counter": Counter({True: 4, False: 1}),
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"mean": 0.8,
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"missing_values": 15,
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"unique_count": 2,
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},
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"$.queries[*].query": {
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"_reverse_lookup": {
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"customer churn": [1],
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"customer feedback": [15],
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"customer satisfaction": [11],
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"email campaigns": [12],
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"email open rates": [17],
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"email outreach": [10],
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"marketing strategies": [14],
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"new products": [16],
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"product sales": [11],
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"revenue 2022": [9],
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"revenue streams": [16],
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"sales Q1": [0, 7, 8, 13],
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"sales Q2": [7],
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"social impact": [12],
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"social trends": [17],
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"web traffic": [12],
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"website analytics": [17],
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},
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"counter": Counter(
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{
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"sales Q1": 4,
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"customer churn": 1,
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"sales Q2": 1,
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"revenue 2022": 1,
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"email outreach": 1,
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"product sales": 1,
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"customer satisfaction": 1,
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"social impact": 1,
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"email campaigns": 1,
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"web traffic": 1,
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"marketing strategies": 1,
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"customer feedback": 1,
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"revenue streams": 1,
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"new products": 1,
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"social trends": 1,
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"email open rates": 1,
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"website analytics": 1,
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}
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),
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"missing_values": 0,
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"str_max_length": 21,
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"str_mean_length": 13.15,
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"str_min_length": 8,
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"str_std_length": 3.8376425054973518,
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"unique_count": 17,
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},
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"$.queries[*].results_limit": {
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"_reverse_lookup": {
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5: [17],
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10: [0, 1, 7, 7, 8, 9, 10, 11, 11, 12, 12, 12, 13, 15, 16, 16, 17, 17],
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15: [14],
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},
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"counter": Counter({10: 18, 15: 1, 5: 1}),
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"max": 15,
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"mean": 10.0,
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"min": 5,
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"missing_values": 0,
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"std": 1.5811388300841898,
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"unique_count": 3,
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},
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"$.queries[*].source_type.enum": {
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"_reverse_lookup": {
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"CRM": [0, 7, 8, 11, 13, 16],
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"EMAIL": [10, 11, 12, 15, 17],
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"SOCIAL_MEDIA": [12, 17],
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"WEB": [1, 7, 9, 12, 14, 16, 17],
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},
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"counter": Counter({"WEB": 7, "CRM": 6, "EMAIL": 5, "SOCIAL_MEDIA": 2}),
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"missing_values": 0,
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"str_max_length": 12,
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"str_mean_length": 4.4,
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"str_min_length": 3,
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"str_std_length": 2.672077843177477,
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"unique_count": 4,
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},
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"$.queries[*].tags": {
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"_reverse_lookup": {},
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"counter": Counter(),
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"missing_values": 16,
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"unique_count": 0,
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},
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"$.queries[*].tags.length": {
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"_reverse_lookup": {1: [15, 17], 2: [10, 14]},
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"counter": Counter({2: 2, 1: 2}),
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"max": 2,
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"mean": 1.5,
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"min": 1,
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"missing_values": 0,
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"std": 0.5,
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"unique_count": 2,
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},
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"$.queries[*].tags[*]": {
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"_reverse_lookup": {
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"2022": [10],
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"2023": [14],
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"analytics": [17],
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"feedback": [15],
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"outreach": [10],
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"strategy": [14],
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},
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"counter": Counter(
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{
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"outreach": 1,
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"2022": 1,
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"strategy": 1,
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"2023": 1,
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"feedback": 1,
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"analytics": 1,
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}
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),
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"missing_values": 0,
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"str_max_length": 9,
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"str_mean_length": 6.833333333333333,
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"str_min_length": 4,
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"str_std_length": 2.034425935955618,
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"unique_count": 6,
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},
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"$.user_id": {
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"_reverse_lookup": {
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"user_1": [0],
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"user_10": [10],
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"user_11": [11],
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"user_12": [12],
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"user_13": [13],
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"user_14": [14],
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"user_15": [15],
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"user_16": [16],
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"user_17": [17],
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"user_2": [1],
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"user_7": [7],
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"user_8": [8],
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"user_9": [9],
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},
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"counter": Counter(
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{
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"user_1": 1,
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"user_2": 1,
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"user_7": 1,
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"user_8": 1,
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"user_9": 1,
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"user_10": 1,
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"user_11": 1,
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"user_12": 1,
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"user_13": 1,
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"user_14": 1,
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"user_15": 1,
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"user_16": 1,
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"user_17": 1,
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}
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),
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"missing_values": 0,
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"str_max_length": 7,
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"str_mean_length": 6.615384615384615,
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"str_min_length": 6,
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"str_std_length": 0.48650425541052295,
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"unique_count": 13,
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},
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"_is_json_": {
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"_reverse_lookup": {
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False: [2, 4],
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True: [0, 1, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17],
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},
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"counter": Counter({True: 16, False: 2}),
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"mean": 0.8888888888888888,
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"missing_values": 0,
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"unique_count": 2,
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},
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"_is_valid_": {
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"_reverse_lookup": {
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False: [3, 5, 6],
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True: [0, 1, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17],
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},
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"counter": Counter({True: 13, False: 3}),
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"mean": 0.8125,
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"missing_values": 0,
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"unique_count": 2,
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},
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"_validation_error_": {
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"_reverse_lookup": {
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"$.queries.[*].is_priority.bool_parsing": [6],
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"$.queries.[*].source_type.enum": [3],
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"$.user_id.missing": [5],
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},
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"counter": Counter(
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{
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"$.queries.[*].source_type.enum": 1,
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"$.user_id.missing": 1,
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"$.queries.[*].is_priority.bool_parsing": 1,
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}
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),
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"missing_values": 0,
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"str_max_length": 38,
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"str_mean_length": 28.333333333333332,
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"str_min_length": 17,
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"str_std_length": 8.653836657164781,
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"unique_count": 3,
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},
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}
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@@ -0,0 +1,65 @@
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import streamlit as st
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from stats_dict import stats_dict
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import json
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# Sample data
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query_data = {i: line for i, line in enumerate(open("test.jsonl", "r"))}
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# Initialize selected keys
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selected_keys = {}
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# Function to get lines
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def get_lines(stats_key, keys):
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indices = []
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for key in keys:
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indices.extend(stats_dict[stats_key]["_reverse_lookup"][key])
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return "\n".join([query_data[i] for i in indices])
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# Function to render dropdown and button
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def render_dropdown_and_button(stats_key):
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st.subheader(f"Stats for `{stats_key}`")
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st.json(stats_dict[stats_key]["counter"])
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st.json(
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{k: v for k, v in stats_dict[stats_key].items() if isinstance(v, (int, float))}
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)
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st.subheader("Histogram")
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st.bar_chart(stats_dict[stats_key]["counter"], use_container_width=True)
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st.subheader("Select keys to view lines")
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options = list(stats_dict[stats_key]["counter"].keys())
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selected_keys[stats_key] = st.multiselect(
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f"View samples with {stats_key}",
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options,
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default=selected_keys.get(stats_key, []),
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)
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if st.button(f"Show Selected for {stats_key}"):
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st.code(get_lines(stats_key, selected_keys[stats_key]))
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# Sidebar for navigation
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st.sidebar.title("Navigation")
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page = st.sidebar.selectbox(
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"Select a page:",
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["Validation Stats", "Individual Path Views"],
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)
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# Main Streamlit App
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st.title("Query Data Visualizer")
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# Validation Stats
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if page == "Validation Stats":
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st.header("Validation Stats")
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for key in [k for k in stats_dict.keys() if k.startswith("_")]:
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render_dropdown_and_button(key)
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# Individual Path Views
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elif page == "Individual Path Views":
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st.header("Individual Path Views")
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path = st.selectbox(
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"Choose a path:",
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[key for key in stats_dict.keys() if not key.startswith("_")],
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)
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if "counter" in stats_dict[path]:
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render_dropdown_and_button(path)
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