Delete src/streamlit_app.py
Browse files- src/streamlit_app.py +0 -132
src/streamlit_app.py
DELETED
@@ -1,132 +0,0 @@
|
|
1 |
-
# streamlit_app.py
|
2 |
-
# A minimal Streamlit app rebuilt with the LangChain framework.
|
3 |
-
|
4 |
-
import streamlit as st
|
5 |
-
import torch
|
6 |
-
from transformers import pipeline
|
7 |
-
|
8 |
-
# Updated LangChain imports for modern versions
|
9 |
-
from langchain_community.llms import HuggingFacePipeline
|
10 |
-
from langchain.prompts import PromptTemplate
|
11 |
-
from langchain.chains import LLMChain
|
12 |
-
from langchain.memory import ConversationBufferMemory
|
13 |
-
|
14 |
-
# -----------------------------------------------------------------------------
|
15 |
-
# CORE MODEL LOGIC (Rebuilt with LangChain)
|
16 |
-
# -----------------------------------------------------------------------------
|
17 |
-
class LangChainBot:
|
18 |
-
def __init__(self):
|
19 |
-
"""
|
20 |
-
Loads the models and wraps them in LangChain components.
|
21 |
-
"""
|
22 |
-
try:
|
23 |
-
# 1. Load the base Hugging Face pipelines
|
24 |
-
generator_pipeline = pipeline(
|
25 |
-
"text2text-generation",
|
26 |
-
model="ai4bharat/IndicBARTSS",
|
27 |
-
device=0 if torch.cuda.is_available() else -1,
|
28 |
-
torch_dtype=(torch.float16 if torch.cuda.is_available() else torch.float32),
|
29 |
-
max_new_tokens=150,
|
30 |
-
repetition_penalty=1.2
|
31 |
-
)
|
32 |
-
|
33 |
-
# Added `trust_remote_code=True` to allow the special translator model to load.
|
34 |
-
self.translator = pipeline(
|
35 |
-
"translation",
|
36 |
-
model="ai4bharat/indictrans2-indic-indic-1B",
|
37 |
-
device=0 if torch.cuda.is_available() else -1,
|
38 |
-
trust_remote_code=True
|
39 |
-
)
|
40 |
-
|
41 |
-
# 2. Wrap the generator in a LangChain LLM object
|
42 |
-
llm = HuggingFacePipeline(pipeline=generator_pipeline)
|
43 |
-
|
44 |
-
# 3. Create a Prompt Template
|
45 |
-
template = """
|
46 |
-
You are a helpful conversational AI. Respond to the user's message.
|
47 |
-
|
48 |
-
{history}
|
49 |
-
मनुष्य: {input}
|
50 |
-
सहायक:
|
51 |
-
"""
|
52 |
-
prompt_template = PromptTemplate(input_variables=["history", "input"], template=template)
|
53 |
-
|
54 |
-
# 4. Set up conversational memory
|
55 |
-
self.memory = ConversationBufferMemory(memory_key="history")
|
56 |
-
|
57 |
-
# 5. Create the final LLMChain
|
58 |
-
self.chain = LLMChain(
|
59 |
-
llm=llm,
|
60 |
-
prompt=prompt_template,
|
61 |
-
verbose=True,
|
62 |
-
memory=self.memory
|
63 |
-
)
|
64 |
-
|
65 |
-
except Exception as e:
|
66 |
-
st.error(f"Fatal: Could not load models. Error: {e}")
|
67 |
-
self.chain = None
|
68 |
-
self.translator = None
|
69 |
-
|
70 |
-
def _translate(self, text, source_lang, target_lang):
|
71 |
-
"""Translation logic remains the same."""
|
72 |
-
if not self.translator or source_lang == target_lang:
|
73 |
-
return text
|
74 |
-
try:
|
75 |
-
codes = {'english': 'eng_Latn', 'hindi': 'hin_Deva', 'tamil': 'tam_Taml', 'telugu': 'tel_Telu'}
|
76 |
-
result = self.translator(text, src_lang=codes[source_lang], tgt_lang=codes[target_lang])
|
77 |
-
return result[0]['translation_text']
|
78 |
-
except Exception as e:
|
79 |
-
st.warning(f"Translation failed. Error: {e}")
|
80 |
-
return text
|
81 |
-
|
82 |
-
def get_response(self, user_message, input_lang, output_lang):
|
83 |
-
"""The main function to get a response."""
|
84 |
-
if not self.chain:
|
85 |
-
return "Error: The LangChain chain is not initialized."
|
86 |
-
|
87 |
-
hindi_message = self._translate(user_message, input_lang, 'hindi')
|
88 |
-
hindi_response = self.chain.run(hindi_message)
|
89 |
-
final_response = self._translate(hindi_response, 'hindi', output_lang)
|
90 |
-
|
91 |
-
return final_response
|
92 |
-
|
93 |
-
# -----------------------------------------------------------------------------
|
94 |
-
# MINIMAL STREAMLIT UI (This part remains mostly the same)
|
95 |
-
# -----------------------------------------------------------------------------
|
96 |
-
|
97 |
-
st.set_page_config(layout="centered")
|
98 |
-
st.title("LangChain Model Interface")
|
99 |
-
|
100 |
-
@st.cache_resource
|
101 |
-
def load_bot():
|
102 |
-
return LangChainBot()
|
103 |
-
|
104 |
-
bot = load_bot()
|
105 |
-
|
106 |
-
if bot and bot.chain: # Only show the UI if the bot loaded successfully
|
107 |
-
st.markdown("---")
|
108 |
-
language_options = ["english", "hindi", "tamil", "telugu"]
|
109 |
-
col1, col2 = st.columns(2)
|
110 |
-
with col1:
|
111 |
-
input_lang = st.selectbox("Input Language", options=language_options, index=0)
|
112 |
-
with col2:
|
113 |
-
output_lang = st.selectbox("Output Language", options=language_options, index=1)
|
114 |
-
|
115 |
-
user_input = st.text_area("Your Message:", height=100)
|
116 |
-
|
117 |
-
if st.button("Get Response"):
|
118 |
-
if user_input:
|
119 |
-
with st.spinner("LangChain is processing your request..."):
|
120 |
-
response = bot.get_response(user_input, input_lang, output_lang)
|
121 |
-
st.markdown("### Model Response:")
|
122 |
-
st.info(response)
|
123 |
-
else:
|
124 |
-
st.warning("Please enter a message.")
|
125 |
-
|
126 |
-
# Add a button to clear LangChain's memory
|
127 |
-
if st.button("Clear Conversation Memory"):
|
128 |
-
if hasattr(bot, 'memory'):
|
129 |
-
bot.memory.clear()
|
130 |
-
st.success("Conversation memory has been cleared.")
|
131 |
-
else:
|
132 |
-
st.error("Application could not start. Please check the logs.")
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|