Rename streamlit_merged.py to app.py

streamlit_merged.py → app.py

@@ -1,273 +1,273 @@
-import streamlit as st
-import os
-import openai
-from sklearn.metrics.pairwise import cosine_similarity
-import numpy as np
-from PyPDF2 import PdfReader
-import json
-import pickle
-from pathlib import Path
-from pydub import AudioSegment
-from groq import Groq
-from typing import List, Dict
-import tempfile
-
-ffmpeg_path = r"C:\Users\AMAR\Downloads\ffmpeg-7.0.2-essentials_build\ffmpeg-7.0.2-essentials_build\bin\ffmpeg.exe"
-os.environ["PATH"] += os.pathsep + os.path.dirname(ffmpeg_path)
-AudioSegment.converter = ffmpeg_path
-class VoiceStockMarketEvaluator:
-    def __init__(self, openai_api_key, groq_api_key):
-        # OpenAI configuration
-        self.openai_api_key = openai_api_key
-        self.pdf_path = "STOCK1.pdf"  # Update this to your PDF path in Streamlit
-        openai.api_key = self.openai_api_key
-        self.llm_model = "gpt-3.5-turbo"
-
-        # Groq configuration for audio transcription
-        self.groq_api_key = groq_api_key
-        self.groq_client = Groq(api_key=self.groq_api_key)
-        self.whisper_model = 'whisper-large-v3'
-
-        # Questions
-        self.questions = {
-            1: "What are different major investors?",
-            2: "Who are major traders?",
-            3: "who are participants of stock makets?"
-        }
-
-        # Load embeddings
-        self.load_embeddings()
-
-    def load_embeddings(self):
-        """Load embeddings from pickle file or create if not exists"""
-        pickle_path = Path('stock_market_embeddings.pkl')
-        
-        if pickle_path.exists():
-            with open(pickle_path, 'rb') as f:
-                data = pickle.load(f)
-                self.pdf_content = data['content']
-                self.pdf_chunks = data['chunks']
-                self.pdf_embeddings = data['embeddings']
-        else:
-            self.create_and_save_embeddings(pickle_path)
-
-    def create_and_save_embeddings(self, pickle_path):
-        """Create embeddings and save to pickle file"""
-        reader = PdfReader(self.pdf_path)
-        self.pdf_content = ""
-        for page in reader.pages:
-            self.pdf_content += page.extract_text()
-        
-        self.pdf_chunks = self._chunk_text(self.pdf_content)
-        self.pdf_embeddings = self.get_openai_embeddings(self.pdf_chunks)
-        
-        data = {
-            'content': self.pdf_content,
-            'chunks': self.pdf_chunks,
-            'embeddings': self.pdf_embeddings
-        }
-        with open(pickle_path, 'wb') as f:
-            pickle.dump(data, f)
-
-    def get_openai_embeddings(self, texts):
-        """Generate embeddings using OpenAI API"""
-        response = openai.Embedding.create(
-            model="text-embedding-ada-002",
-            input=texts
-        )
-        return [embedding['embedding'] for embedding in response['data']]
-
-    def _chunk_text(self, text, chunk_size=500, overlap=50):
-        """Split text into overlapping chunks"""
-        words = text.split()
-        chunks = []
-        for i in range(0, len(words), chunk_size - overlap):
-            chunk = ' '.join(words[i:i + chunk_size])
-            chunks.append(chunk)
-        return chunks
-
-    def process_audio_file(self, uploaded_file):
-        """Process uploaded audio file and convert if necessary"""
-        allowed_formats = ["flac", "mp3", "mp4", "mpeg", "mpga", "m4a", "ogg", "opus", "wav", "webm"]
-        
-        # Create a temporary directory to store the processed file
-        with tempfile.TemporaryDirectory() as temp_dir:
-            # Get the original file extension
-            original_extension = uploaded_file.name.split('.')[-1].lower()
-            temp_file_path = os.path.join(temp_dir, f"audio_file.{original_extension}")
-            
-            # Save the uploaded file
-            with open(temp_file_path, 'wb') as f:
-                f.write(uploaded_file.getbuffer())
-            
-            # If the format is not supported, convert to WAV
-            if original_extension not in allowed_formats:
-                output_path = os.path.join(temp_dir, "converted_audio.wav")
-                audio = AudioSegment.from_file(temp_file_path)
-                audio.export(output_path, format="wav")
-                final_path = output_path
-            else:
-                final_path = temp_file_path
-            
-            # Read the file for transcription
-            with open(final_path, 'rb') as audio_file:
-                return self.audio_to_text(final_path)
-
-    def audio_to_text(self, filepath):
-        """Transcribe audio to text using Groq"""
-        with open(filepath, "rb") as file:
-            translation = self.groq_client.audio.translations.create(
-                file=(filepath, file.read()),
-                model=self.whisper_model,
-            )
-        return translation.text
-
-    def _find_relevant_context(self, question, answer, top_k=3):
-        """Find relevant PDF chunks for the question-answer pair"""
-        search_text = f"{question} {answer}"
-        search_embedding = self.get_openai_embeddings([search_text])[0]
-        similarities = cosine_similarity([search_embedding], self.pdf_embeddings)[0]
-        top_indices = similarities.argsort()[-top_k:][::-1]
-        return ' '.join(self.pdf_chunks[i] for i in top_indices)
-
-    def evaluate_answer(self, question_num, user_answer):
-        """Evaluate user answer for a given question number"""
-        if question_num not in self.questions:
-            raise ValueError("Invalid question number")
-        
-        question = self.questions[question_num]
-        relevant_context = self._find_relevant_context(question, user_answer)
-        
-        prompt = f"""
-        You are an expert evaluating answers about the stock market. Compare the answer with the reference material and provide detailed analysis.
-
-        Question: {question}
-        
-        Reference Material:
-        {relevant_context}
-        
-        Student Answer:
-        {user_answer}
-
-        Analyze this answer carefully and provide:
-        1. Points that are correct according to the reference material
-        2. Points that are incorrect or need clarification
-        3. Important points from the reference material that were missing
-
-        Provide your analysis in JSON format:
-        {{
-            "correct_points": ["point1", "point2"],
-            "incorrect_points": {{"incorrect_statement": "correction_based_on_reference"}},
-            "missing_points": ["point1", "point2"],
-            "explanation": "Brief explanation of the score"
-        }}
-        """
-        
-        response = openai.ChatCompletion.create(
-            model=self.llm_model,
-            messages=[{"role": "user", "content": prompt}],
-            temperature=0.3
-        )
-        
-        try:
-            return json.loads(response.choices[0].message.content)
-        except json.JSONDecodeError:
-            return {"error": "Could not parse response"}
-
-def main():
-    st.set_page_config(page_title="Voice-based Stock Market Evaluator", layout="wide")
-    
-    st.title("Voice-based Stock Market Evaluator")
-    
-    # Create sidebar for API keys
-    st.sidebar.header("API Configuration")
-    
-    # Use session state to persist API keys
-    if 'openai_key' not in st.session_state:
-        st.session_state['openai_key'] = ''
-    if 'groq_key' not in st.session_state:
-        st.session_state['groq_key'] = ''
-    
-    # API key inputs in sidebar
-    openai_key = st.sidebar.text_input(
-        "OpenAI API Key",
-        type="password",
-        value=st.session_state['openai_key'],
-        help="Enter your OpenAI API key to use the evaluation features"
-    )
-    groq_key = st.sidebar.text_input(
-        "Groq API Key",
-        type="password",
-        value=st.session_state['groq_key'],
-        help="Enter your Groq API key for audio transcription"
-    )
-    
-    # Update session state
-    st.session_state['openai_key'] = openai_key
-    st.session_state['groq_key'] = groq_key
-
-    # Check if API keys are provided
-    if not openai_key or not groq_key:
-        st.warning("Please enter both API keys in the sidebar to use the application.")
-        return
-
-    try:
-        # Initialize the evaluator with API keys
-        evaluator = VoiceStockMarketEvaluator(openai_key, groq_key)
-
-        st.write("Upload an audio file with your answer to get evaluated!")
-
-        # Display questions
-        st.header("Available Questions")
-        question_num = st.radio(
-            "Select a question:",
-            options=list(evaluator.questions.keys()),
-            format_func=lambda x: f"Question {x}: {evaluator.questions[x]}"
-        )
-
-        # File uploader
-        uploaded_file = st.file_uploader("Upload your audio response", type=None)  # Accept all file types
-
-        if uploaded_file is not None:
-            with st.spinner("Processing audio file..."):
-                try:
-                    # Process audio and get transcription
-                    transcribed_text = evaluator.process_audio_file(uploaded_file)
-                    
-                    st.subheader("Transcribed Text")
-                    st.write(transcribed_text)
-
-                    # Evaluate answer
-                    with st.spinner("Analyzing your answer..."):
-                        result = evaluator.evaluate_answer(question_num, transcribed_text)
-
-                    # Display results
-                    st.subheader("Analysis Results")
-                    
-                    # Correct points
-                    st.success("Correct Points:")
-                    for point in result.get("correct_points", []):
-                        st.write(f"✓ {point}")
-
-                    # Incorrect points
-                    st.error("Points Needing Correction:")
-                    for statement, correction in result.get("incorrect_points", {}).items():
-                        st.write(f"✗ {statement}")
-                        st.write(f"Correction: {correction}")
-
-                    # Missing points
-                    st.warning("Missing Points:")
-                    for point in result.get("missing_points", []):
-                        st.write(f"• {point}")
-
-                    # Score and explanation
-                    st.info(f"Explanation: {result.get('explanation', 'No explanation provided')}")
-
-                except Exception as e:
-                    st.error(f"Error processing file: {str(e)}")
-
-    except Exception as e:
-        st.error(f"Error initializing the evaluator: {str(e)}")
-
-if __name__ == "__main__":
+import streamlit as st
+import os
+import openai
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
+from PyPDF2 import PdfReader
+import json
+import pickle
+from pathlib import Path
+from pydub import AudioSegment
+from groq import Groq
+from typing import List, Dict
+import tempfile
+
+#ffmpeg_path = r"C:\Users\AMAR\Downloads\ffmpeg-7.0.2-essentials_build\ffmpeg-7.0.2-essentials_build\bin\ffmpeg.exe"
+#os.environ["PATH"] += os.pathsep + os.path.dirname(ffmpeg_path)
+#AudioSegment.converter = ffmpeg_path
+class VoiceStockMarketEvaluator:
+    def __init__(self, openai_api_key, groq_api_key):
+        # OpenAI configuration
+        self.openai_api_key = openai_api_key
+        self.pdf_path = "STOCK1.pdf"  # Update this to your PDF path in Streamlit
+        openai.api_key = self.openai_api_key
+        self.llm_model = "gpt-3.5-turbo"
+
+        # Groq configuration for audio transcription
+        self.groq_api_key = groq_api_key
+        self.groq_client = Groq(api_key=self.groq_api_key)
+        self.whisper_model = 'whisper-large-v3'
+
+        # Questions
+        self.questions = {
+            1: "What are different major investors?",
+            2: "Who are major traders?",
+            3: "who are participants of stock makets?"
+        }
+
+        # Load embeddings
+        self.load_embeddings()
+
+    def load_embeddings(self):
+        """Load embeddings from pickle file or create if not exists"""
+        pickle_path = Path('stock_market_embeddings.pkl')
+        
+        if pickle_path.exists():
+            with open(pickle_path, 'rb') as f:
+                data = pickle.load(f)
+                self.pdf_content = data['content']
+                self.pdf_chunks = data['chunks']
+                self.pdf_embeddings = data['embeddings']
+        else:
+            self.create_and_save_embeddings(pickle_path)
+
+    def create_and_save_embeddings(self, pickle_path):
+        """Create embeddings and save to pickle file"""
+        reader = PdfReader(self.pdf_path)
+        self.pdf_content = ""
+        for page in reader.pages:
+            self.pdf_content += page.extract_text()
+        
+        self.pdf_chunks = self._chunk_text(self.pdf_content)
+        self.pdf_embeddings = self.get_openai_embeddings(self.pdf_chunks)
+        
+        data = {
+            'content': self.pdf_content,
+            'chunks': self.pdf_chunks,
+            'embeddings': self.pdf_embeddings
+        }
+        with open(pickle_path, 'wb') as f:
+            pickle.dump(data, f)
+
+    def get_openai_embeddings(self, texts):
+        """Generate embeddings using OpenAI API"""
+        response = openai.Embedding.create(
+            model="text-embedding-ada-002",
+            input=texts
+        )
+        return [embedding['embedding'] for embedding in response['data']]
+
+    def _chunk_text(self, text, chunk_size=500, overlap=50):
+        """Split text into overlapping chunks"""
+        words = text.split()
+        chunks = []
+        for i in range(0, len(words), chunk_size - overlap):
+            chunk = ' '.join(words[i:i + chunk_size])
+            chunks.append(chunk)
+        return chunks
+
+    def process_audio_file(self, uploaded_file):
+        """Process uploaded audio file and convert if necessary"""
+        allowed_formats = ["flac", "mp3", "mp4", "mpeg", "mpga", "m4a", "ogg", "opus", "wav", "webm"]
+        
+        # Create a temporary directory to store the processed file
+        with tempfile.TemporaryDirectory() as temp_dir:
+            # Get the original file extension
+            original_extension = uploaded_file.name.split('.')[-1].lower()
+            temp_file_path = os.path.join(temp_dir, f"audio_file.{original_extension}")
+            
+            # Save the uploaded file
+            with open(temp_file_path, 'wb') as f:
+                f.write(uploaded_file.getbuffer())
+            
+            # If the format is not supported, convert to WAV
+            if original_extension not in allowed_formats:
+                output_path = os.path.join(temp_dir, "converted_audio.wav")
+                audio = AudioSegment.from_file(temp_file_path)
+                audio.export(output_path, format="wav")
+                final_path = output_path
+            else:
+                final_path = temp_file_path
+            
+            # Read the file for transcription
+            with open(final_path, 'rb') as audio_file:
+                return self.audio_to_text(final_path)
+
+    def audio_to_text(self, filepath):
+        """Transcribe audio to text using Groq"""
+        with open(filepath, "rb") as file:
+            translation = self.groq_client.audio.translations.create(
+                file=(filepath, file.read()),
+                model=self.whisper_model,
+            )
+        return translation.text
+
+    def _find_relevant_context(self, question, answer, top_k=3):
+        """Find relevant PDF chunks for the question-answer pair"""
+        search_text = f"{question} {answer}"
+        search_embedding = self.get_openai_embeddings([search_text])[0]
+        similarities = cosine_similarity([search_embedding], self.pdf_embeddings)[0]
+        top_indices = similarities.argsort()[-top_k:][::-1]
+        return ' '.join(self.pdf_chunks[i] for i in top_indices)
+
+    def evaluate_answer(self, question_num, user_answer):
+        """Evaluate user answer for a given question number"""
+        if question_num not in self.questions:
+            raise ValueError("Invalid question number")
+        
+        question = self.questions[question_num]
+        relevant_context = self._find_relevant_context(question, user_answer)
+        
+        prompt = f"""
+        You are an expert evaluating answers about the stock market. Compare the answer with the reference material and provide detailed analysis.
+
+        Question: {question}
+        
+        Reference Material:
+        {relevant_context}
+        
+        Student Answer:
+        {user_answer}
+
+        Analyze this answer carefully and provide:
+        1. Points that are correct according to the reference material
+        2. Points that are incorrect or need clarification
+        3. Important points from the reference material that were missing
+
+        Provide your analysis in JSON format:
+        {{
+            "correct_points": ["point1", "point2"],
+            "incorrect_points": {{"incorrect_statement": "correction_based_on_reference"}},
+            "missing_points": ["point1", "point2"],
+            "explanation": "Brief explanation of the score"
+        }}
+        """
+        
+        response = openai.ChatCompletion.create(
+            model=self.llm_model,
+            messages=[{"role": "user", "content": prompt}],
+            temperature=0.3
+        )
+        
+        try:
+            return json.loads(response.choices[0].message.content)
+        except json.JSONDecodeError:
+            return {"error": "Could not parse response"}
+
+def main():
+    st.set_page_config(page_title="Voice-based Stock Market Evaluator", layout="wide")
+    
+    st.title("Voice-based Stock Market Evaluator")
+    
+    # Create sidebar for API keys
+    st.sidebar.header("API Configuration")
+    
+    # Use session state to persist API keys
+    if 'openai_key' not in st.session_state:
+        st.session_state['openai_key'] = ''
+    if 'groq_key' not in st.session_state:
+        st.session_state['groq_key'] = ''
+    
+    # API key inputs in sidebar
+    openai_key = st.sidebar.text_input(
+        "OpenAI API Key",
+        type="password",
+        value=st.session_state['openai_key'],
+        help="Enter your OpenAI API key to use the evaluation features"
+    )
+    groq_key = st.sidebar.text_input(
+        "Groq API Key",
+        type="password",
+        value=st.session_state['groq_key'],
+        help="Enter your Groq API key for audio transcription"
+    )
+    
+    # Update session state
+    st.session_state['openai_key'] = openai_key
+    st.session_state['groq_key'] = groq_key
+
+    # Check if API keys are provided
+    if not openai_key or not groq_key:
+        st.warning("Please enter both API keys in the sidebar to use the application.")
+        return
+
+    try:
+        # Initialize the evaluator with API keys
+        evaluator = VoiceStockMarketEvaluator(openai_key, groq_key)
+
+        st.write("Upload an audio file with your answer to get evaluated!")
+
+        # Display questions
+        st.header("Available Questions")
+        question_num = st.radio(
+            "Select a question:",
+            options=list(evaluator.questions.keys()),
+            format_func=lambda x: f"Question {x}: {evaluator.questions[x]}"
+        )
+
+        # File uploader
+        uploaded_file = st.file_uploader("Upload your audio response", type=None)  # Accept all file types
+
+        if uploaded_file is not None:
+            with st.spinner("Processing audio file..."):
+                try:
+                    # Process audio and get transcription
+                    transcribed_text = evaluator.process_audio_file(uploaded_file)
+                    
+                    st.subheader("Transcribed Text")
+                    st.write(transcribed_text)
+
+                    # Evaluate answer
+                    with st.spinner("Analyzing your answer..."):
+                        result = evaluator.evaluate_answer(question_num, transcribed_text)
+
+                    # Display results
+                    st.subheader("Analysis Results")
+                    
+                    # Correct points
+                    st.success("Correct Points:")
+                    for point in result.get("correct_points", []):
+                        st.write(f"✓ {point}")
+
+                    # Incorrect points
+                    st.error("Points Needing Correction:")
+                    for statement, correction in result.get("incorrect_points", {}).items():
+                        st.write(f"✗ {statement}")
+                        st.write(f"Correction: {correction}")
+
+                    # Missing points
+                    st.warning("Missing Points:")
+                    for point in result.get("missing_points", []):
+                        st.write(f"• {point}")
+
+                    # Score and explanation
+                    st.info(f"Explanation: {result.get('explanation', 'No explanation provided')}")
+
+                except Exception as e:
+                    st.error(f"Error processing file: {str(e)}")
+
+    except Exception as e:
+        st.error(f"Error initializing the evaluator: {str(e)}")
+
+if __name__ == "__main__":
     main()