Delete src/streamlit_app.py

src/streamlit_app.py

@@ -1,132 +0,0 @@
-# streamlit_app.py
-# A minimal Streamlit app rebuilt with the LangChain framework.
-
-import streamlit as st
-import torch
-from transformers import pipeline
-
-# Updated LangChain imports for modern versions
-from langchain_community.llms import HuggingFacePipeline
-from langchain.prompts import PromptTemplate
-from langchain.chains import LLMChain
-from langchain.memory import ConversationBufferMemory
-
-# -----------------------------------------------------------------------------
-# CORE MODEL LOGIC (Rebuilt with LangChain)
-# -----------------------------------------------------------------------------
-class LangChainBot:
-    def __init__(self):
-        """
-        Loads the models and wraps them in LangChain components.
-        """
-        try:
-            # 1. Load the base Hugging Face pipelines
-            generator_pipeline = pipeline(
-                "text2text-generation",
-                model="ai4bharat/IndicBARTSS",
-                device=0 if torch.cuda.is_available() else -1,
-                torch_dtype=(torch.float16 if torch.cuda.is_available() else torch.float32),
-                max_new_tokens=150,
-                repetition_penalty=1.2
-            )
-            
-            # Added `trust_remote_code=True` to allow the special translator model to load.
-            self.translator = pipeline(
-                "translation",
-                model="ai4bharat/indictrans2-indic-indic-1B",
-                device=0 if torch.cuda.is_available() else -1,
-                trust_remote_code=True
-            )
-
-            # 2. Wrap the generator in a LangChain LLM object
-            llm = HuggingFacePipeline(pipeline=generator_pipeline)
-
-            # 3. Create a Prompt Template
-            template = """
-            You are a helpful conversational AI. Respond to the user's message.
-
-            {history}
-            मनुष्य: {input}
-            सहायक:
-            """
-            prompt_template = PromptTemplate(input_variables=["history", "input"], template=template)
-
-            # 4. Set up conversational memory
-            self.memory = ConversationBufferMemory(memory_key="history")
-
-            # 5. Create the final LLMChain
-            self.chain = LLMChain(
-                llm=llm,
-                prompt=prompt_template,
-                verbose=True,
-                memory=self.memory
-            )
-            
-        except Exception as e:
-            st.error(f"Fatal: Could not load models. Error: {e}")
-            self.chain = None
-            self.translator = None
-
-    def _translate(self, text, source_lang, target_lang):
-        """Translation logic remains the same."""
-        if not self.translator or source_lang == target_lang:
-            return text
-        try:
-            codes = {'english': 'eng_Latn', 'hindi': 'hin_Deva', 'tamil': 'tam_Taml', 'telugu': 'tel_Telu'}
-            result = self.translator(text, src_lang=codes[source_lang], tgt_lang=codes[target_lang])
-            return result[0]['translation_text']
-        except Exception as e:
-            st.warning(f"Translation failed. Error: {e}")
-            return text
-
-    def get_response(self, user_message, input_lang, output_lang):
-        """The main function to get a response."""
-        if not self.chain:
-            return "Error: The LangChain chain is not initialized."
-
-        hindi_message = self._translate(user_message, input_lang, 'hindi')
-        hindi_response = self.chain.run(hindi_message)
-        final_response = self._translate(hindi_response, 'hindi', output_lang)
-
-        return final_response
-
-# -----------------------------------------------------------------------------
-# MINIMAL STREAMLIT UI (This part remains mostly the same)
-# -----------------------------------------------------------------------------
-
-st.set_page_config(layout="centered")
-st.title("LangChain Model Interface")
-
-@st.cache_resource
-def load_bot():
-    return LangChainBot()
-
-bot = load_bot()
-
-if bot and bot.chain: # Only show the UI if the bot loaded successfully
-    st.markdown("---")
-    language_options = ["english", "hindi", "tamil", "telugu"]
-    col1, col2 = st.columns(2)
-    with col1:
-        input_lang = st.selectbox("Input Language", options=language_options, index=0)
-    with col2:
-        output_lang = st.selectbox("Output Language", options=language_options, index=1)
-
-    user_input = st.text_area("Your Message:", height=100)
-
-    if st.button("Get Response"):
-        if user_input:
-            with st.spinner("LangChain is processing your request..."):
-                response = bot.get_response(user_input, input_lang, output_lang)
-                st.markdown("### Model Response:")
-                st.info(response)
-        else:
-            st.warning("Please enter a message.")
-
-    # Add a button to clear LangChain's memory
-    if st.button("Clear Conversation Memory"):
-        if hasattr(bot, 'memory'):
-            bot.memory.clear()
-            st.success("Conversation memory has been cleared.")
-else:
-    st.error("Application could not start. Please check the logs.")