Create app.py

app.py

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+# app.py
+import gradio as gr
+import pandas as pd
+import numpy as np
+import yfinance as yf
+from ta import add_all_ta_features
+from ta.utils import dropna
+from transformers import pipeline, AutoModelForQuestionAnswering, AutoTokenizer
+from sentence_transformers import SentenceTransformer
+import faiss
+
+# Load the dataset from Google Drive
+def load_data():
+    url = "https://drive.google.com/uc?export=download&id=1N1bCVRSacs7_nENJzleqqNRHA22-H9B5"
+    df = pd.read_csv(url)
+    return df
+
+# Preprocess the data
+def preprocess_data(df):
+    df = dropna(df)
+    df = add_all_ta_features(df, open="Open", high="High", low="Low", close="Close", volume="Volume", fillna=True)
+    return df
+
+# Train the FAISS index for RAG
+def train_faiss_index(df):
+    model = SentenceTransformer('all-MiniLM-L6-v2')
+    embeddings = model.encode(df['Close'].astype(str).tolist())
+    dimension = embeddings.shape[1]
+    index = faiss.IndexFlatL2(dimension)
+    index.add(embeddings)
+    return index, model
+
+# Load the QA model
+def load_qa_model():
+    model_name = "deepset/roberta-base-squad2"
+    model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    qa_pipeline = pipeline("question-answering", model=model, tokenizer=tokenizer)
+    return qa_pipeline
+
+# Get technical analysis
+def get_technical_analysis(df):
+    analysis = {
+        "SMA_50": df['Close'].rolling(window=50).mean().iloc[-1],
+        "SMA_200": df['Close'].rolling(window=200).mean().iloc[-1],
+        "RSI": df['momentum_rsi'].iloc[-1],
+        "MACD": df['trend_macd'].iloc[-1],
+    }
+    return analysis
+
+# RAG-based QA function
+def rag_qa(question, df, index, model, qa_pipeline):
+    query_embedding = model.encode([question])
+    distances, indices = index.search(query_embedding, k=1)
+    context = df.iloc[indices[0][0]]['Close']
+    result = qa_pipeline(question=question, context=str(context))
+    return result['answer']
+
+# Gradio Interface
+def trading_analysis_app(question):
+    df = load_data()
+    df = preprocess_data(df)
+    index, model = train_faiss_index(df)
+    qa_pipeline = load_qa_model()
+    
+    analysis = get_technical_analysis(df)
+    answer = rag_qa(question, df, index, model, qa_pipeline)
+    
+    return f"Technical Analysis: {analysis}\n\nAnswer: {answer}"
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=trading_analysis_app,
+    inputs="text",
+    outputs="text",
+    title="RAG-based Trading Analysis",
+    description="Enter your question about ICICIBANK's stock to get technical analysis and answers."
+)
+
+iface.launch()