Spaces:
Sleeping
Sleeping
Safwanahmad619
commited on
Update app.py
Browse files
app.py
CHANGED
@@ -1,10 +1,5 @@
|
|
1 |
import gradio as gr
|
2 |
-
from transformers import pipeline, AutoTokenizer,
|
3 |
-
|
4 |
-
# Load DNA Analysis Model
|
5 |
-
dna_tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t6_8M_UR50D")
|
6 |
-
dna_model = AutoModelForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D")
|
7 |
-
dna_pipeline = pipeline("fill-mask", model=dna_model, tokenizer=dna_tokenizer)
|
8 |
|
9 |
# Load Ethical Inquiry and Learning Support Model
|
10 |
ethics_tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-base")
|
@@ -13,10 +8,8 @@ ethics_pipeline = pipeline("text2text-generation", model=ethics_model, tokenizer
|
|
13 |
|
14 |
# Query Classification
|
15 |
def classify_query(query):
|
16 |
-
"""Classify the query into
|
17 |
-
if "
|
18 |
-
return "dna_analysis"
|
19 |
-
elif "ethics" in query or "privacy" in query:
|
20 |
return "ethical_inquiry"
|
21 |
else:
|
22 |
return "learning_support"
|
@@ -26,16 +19,7 @@ def handle_query(query):
|
|
26 |
"""Route the query to the appropriate model and generate a response."""
|
27 |
task = classify_query(query)
|
28 |
|
29 |
-
if task == "
|
30 |
-
try:
|
31 |
-
# Example DNA sequence processing: Replace X or any part of the sequence with [MASK]
|
32 |
-
masked_sequence = query.replace("X", "[MASK]")
|
33 |
-
output = dna_pipeline(masked_sequence)
|
34 |
-
return f"DNA Analysis Result: {output}"
|
35 |
-
except Exception as e:
|
36 |
-
return f"Error in DNA Analysis: {e}"
|
37 |
-
|
38 |
-
elif task == "ethical_inquiry":
|
39 |
try:
|
40 |
# Ethical guidance response
|
41 |
response = ethics_pipeline(query)
|
@@ -61,7 +45,7 @@ interface = gr.Interface(
|
|
61 |
inputs="text",
|
62 |
outputs="text",
|
63 |
title="BioSphere AI Chatbot",
|
64 |
-
description="A chatbot for
|
65 |
)
|
66 |
|
67 |
# Add Gemmini API Key Integration
|
|
|
1 |
import gradio as gr
|
2 |
+
from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
|
|
|
|
|
|
|
|
|
|
|
3 |
|
4 |
# Load Ethical Inquiry and Learning Support Model
|
5 |
ethics_tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-base")
|
|
|
8 |
|
9 |
# Query Classification
|
10 |
def classify_query(query):
|
11 |
+
"""Classify the query into Ethical Inquiry or Learning Support."""
|
12 |
+
if "ethics" in query or "privacy" in query:
|
|
|
|
|
13 |
return "ethical_inquiry"
|
14 |
else:
|
15 |
return "learning_support"
|
|
|
19 |
"""Route the query to the appropriate model and generate a response."""
|
20 |
task = classify_query(query)
|
21 |
|
22 |
+
if task == "ethical_inquiry":
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
23 |
try:
|
24 |
# Ethical guidance response
|
25 |
response = ethics_pipeline(query)
|
|
|
45 |
inputs="text",
|
46 |
outputs="text",
|
47 |
title="BioSphere AI Chatbot",
|
48 |
+
description="A chatbot for Ethical Guidance and Learning Support in Biotech.",
|
49 |
)
|
50 |
|
51 |
# Add Gemmini API Key Integration
|