added html render

app.py

@@ -1,176 +1,181 @@
-import os
-import time
-import json
-import asyncio
-import numpy as np
-import soundfile as sf
-from datetime import datetime
-from pydub import AudioSegment
-
-# External dependencies.
-from flask import Flask, request, jsonify, send_file
-
-# Import your modules.
-from app.stt import AudioProcessor
-from app.llm import LLMProcessor
-from app.vectorstore import vectorstore
-from app.tts import tts
-from app.utils import remove_stars
-
-# Set a custom user agent.
-os.environ["USER_AGENT"] = "my-app/1.0"
-
-# Rename your class to avoid confusion with Flask's app instance.
-class CancerApp:
-    def __init__(self, vectorstore_index_path):
-        self.audio_processor = AudioProcessor()
-        self.llm_processor = LLMProcessor()
-        self.vectorstore = vectorstore(vectorstore_index_path, Initlize_with=3)
-        self.vectorstore_index = self.vectorstore.load_vectorstore(vectorstore_index_path)
-        # Run an initial search to load vectorstore contents.
-        self.vectorstore.search_vectorstore("cancer", 1)
-        self.tts = tts()
-
-    def text_to_speech(self, message, output_filename, Saved_response=""):
-        search_results = self.vectorstore.search_vectorstore(message, 5)
-        query = Saved_response + message 
-        for i, result in enumerate(search_results, 1):
-            query += f"\n{i}. {result}"
-        
-        try:
-            gemini_response = self.llm_processor.call_gemini_llm("gemini-2.0-flash", query)
-        except Exception as e:
-            print("Gemini error:", e)
-            gemini_response = f"Error: {str(e)}"
-        gemini_response = remove_stars(gemini_response)
-        Saved_response += "message: " + message + "\n" + "gemini_response: " + gemini_response + "\n"
-        self.audio_processor.log_conversation(message, bot_text=gemini_response)
-        audio_data, sample_rate = asyncio.run(self.tts.cpu_stream_to_audio(gemini_response))
-        sf.write(output_filename, audio_data, sample_rate)
-        return Saved_response
-
-    def speech_to_speech(self, audiofile_path, output_filename, Saved_response=""):
-        transcription = self.audio_processor.transcribe_audio(audiofile_path, language="en")
-        search_results = self.vectorstore.search_vectorstore(transcription, 5)
-        query = Saved_response + transcription 
-        for i, result in enumerate(search_results, 1):
-            query += f"\n{i}. {result}"
-        try:
-            gemini_response = self.llm_processor.call_gemini_llm("gemini-2.0-flash", query)
-        except Exception as e:
-            print("Gemini error:", e)
-            gemini_response = f"Error: {str(e)}"
-        gemini_response = remove_stars(gemini_response)
-        Saved_response += "transcription: " + transcription + "\n" + "gemini_response: " + gemini_response + "\n"
-        self.audio_processor.log_conversation(transcription, bot_text=gemini_response)
-        audio_data, sample_rate = asyncio.run(self.tts.cpu_stream_to_audio(gemini_response))
-        sf.write(output_filename, audio_data, sample_rate)
-        return Saved_response
-
-    def speech_to_text(self, audiofile_path, Saved_response=""):
-        try:
-            transcription = self.audio_processor.transcribe_audio(audiofile_path, language="en")
-        except Exception as ex:
-            print(f"Transcription error: {ex}")
-            transcription = ""
-        
-        if not transcription:
-            print("No transcription available; aborting further processing.")
-            return "", Saved_response
-
-        try:
-            search_results = self.vectorstore.search_vectorstore(transcription, 5)
-        except Exception as ex:
-            print(f"Vectorstore search error: {ex}")
-            search_results = []
-        
-        query = f"{Saved_response}{transcription}"
-        for i, result in enumerate(search_results, 1):
-            query += f"\n{i}. {result}"
-        
-        try:
-            gemini_response = self.llm_processor.call_gemini_llm("gemini-2.0-flash", query)
-            gemini_response = remove_stars(gemini_response)
-        except Exception as e:
-            print("Gemini error:", e)
-            gemini_response = f"Error: {str(e)}"
-        gemini_response = remove_stars(gemini_response)
-        Saved_response += f"transcription: {transcription}\n" + f"gemini_response: {gemini_response}\n"
-        self.audio_processor.log_conversation(transcription, bot_text=gemini_response)
-        return gemini_response, Saved_response
-
-    def text_to_text(self, message, Saved_response=""):
-        search_results = self.vectorstore.search_vectorstore(message, 5)
-        query = Saved_response + message 
-        for i, result in enumerate(search_results, 1):
-            query += f"\n{i}. {result}"
-        
-        try:
-            gemini_response = self.llm_processor.call_gemini_llm("gemini-2.0-flash", query)
-            gemini_response = remove_stars(gemini_response)
-        except Exception as e:
-            print("Gemini error:", e)
-            gemini_response = f"Error: {str(e)}"
-        Saved_response += "message: " + message + "\n" + "gemini_response: " + gemini_response + "\n"
-        self.audio_processor.log_conversation(message, bot_text=gemini_response)
-        return gemini_response, Saved_response
-
-# Create Flask API instance.
-flask_app = Flask(__name__)
-
-# Instantiate the CancerApp with your vectorstore path.
-vectordb_path = os.path.join("VectorDB", "vectorstore_20250328_011300")
-cancer_app_instance = CancerApp(vectordb_path)
-
-# Endpoint for text-to-text processing.
-@flask_app.route('/text_to_text', methods=['POST'])
-def api_text_to_text():
-    data = request.get_json()
-    message = data.get("message", "")
-    saved_response = data.get("Saved_response", "")
-    response, saved_response = cancer_app_instance.text_to_text(message, Saved_response=saved_response)
-    return jsonify({"gemini_response": response, "Saved_response": saved_response})
-
-# Endpoint for text-to-speech processing.
-@flask_app.route('/text_to_speech', methods=['POST'])
-def api_text_to_speech():
-    data = request.get_json()
-    message = data.get("message", "")
-    output_filename = data.get("output_filename", "output.wav")
-    saved_response = data.get("Saved_response", "")
-    saved_response = cancer_app_instance.text_to_speech(message, output_filename, Saved_response=saved_response)
-    return jsonify({"output_filename": output_filename, "Saved_response": saved_response})
-
-# Endpoint for speech-to-text processing.
-@flask_app.route('/speech_to_text', methods=['POST'])
-def api_speech_to_text():
-    if 'audiofile' not in request.files:
-        return jsonify({"error": "No audio file provided"}), 400
-    audio_file = request.files['audiofile']
-    # Save uploaded file temporarily.
-    audio_ext = audio_file.filename.split('.')[-1]
-    audio_path = f"temp_audio_input.{audio_ext}"
-    audio_file.save(audio_path)
-    saved_response = request.form.get("Saved_response", "")
-    gemini_response, saved_response = cancer_app_instance.speech_to_text(audio_path, Saved_response=saved_response)
-    os.remove(audio_path)
-    return jsonify({"gemini_response": gemini_response, "Saved_response": saved_response})
-
-# Endpoint for speech-to-speech processing.
-@flask_app.route('/speech_to_speech', methods=['POST'])
-def api_speech_to_speech():
-    if 'audiofile' not in request.files:
-        return jsonify({"error": "No audio file provided"}), 400
-    audio_file = request.files['audiofile']
-    audio_ext = audio_file.filename.split('.')[-1]
-    audio_path = f"temp_audio_input.{audio_ext}"
-    audio_file.save(audio_path)
-    output_filename = request.form.get("output_filename", "speech_output.wav")
-    saved_response = request.form.get("Saved_response", "")
-    saved_response = cancer_app_instance.speech_to_speech(audio_path, output_filename, Saved_response=saved_response)
-    os.remove(audio_path)
-    # Return the output file for download.
-    return send_file(output_filename, as_attachment=True)
-
-if __name__ == '__main__':
-    flask_app.run(host='0.0.0.0', port=7860, debug=True)
+import os
+import time
+import json
+import asyncio
+import numpy as np
+import soundfile as sf
+from datetime import datetime
+from pydub import AudioSegment
+
+# External dependencies.
+from flask import Flask, request, jsonify, send_file
+
+# Import your modules.
+from app.stt import AudioProcessor
+from app.llm import LLMProcessor
+from app.vectorstore import vectorstore
+from app.tts import tts
+from app.utils import remove_stars
+
+# Set a custom user agent.
+os.environ["USER_AGENT"] = "my-app/1.0"
+
+# Rename your class to avoid confusion with Flask's app instance.
+class CancerApp:
+    def __init__(self, vectorstore_index_path):
+        self.audio_processor = AudioProcessor()
+        self.llm_processor = LLMProcessor()
+        self.vectorstore = vectorstore(vectorstore_index_path, Initlize_with=3)
+        self.vectorstore_index = self.vectorstore.load_vectorstore(vectorstore_index_path)
+        # Run an initial search to load vectorstore contents.
+        self.vectorstore.search_vectorstore("cancer", 1)
+        self.tts = tts()
+
+    def text_to_speech(self, message, output_filename, Saved_response=""):
+        search_results = self.vectorstore.search_vectorstore(message, 5)
+        query = Saved_response + message 
+        for i, result in enumerate(search_results, 1):
+            query += f"\n{i}. {result}"
+        
+        try:
+            gemini_response = self.llm_processor.call_gemini_llm("gemini-2.0-flash", query)
+        except Exception as e:
+            print("Gemini error:", e)
+            gemini_response = f"Error: {str(e)}"
+        gemini_response = remove_stars(gemini_response)
+        Saved_response += "message: " + message + "\n" + "gemini_response: " + gemini_response + "\n"
+        self.audio_processor.log_conversation(message, bot_text=gemini_response)
+        audio_data, sample_rate = asyncio.run(self.tts.cpu_stream_to_audio(gemini_response))
+        sf.write(output_filename, audio_data, sample_rate)
+        return Saved_response
+
+    def speech_to_speech(self, audiofile_path, output_filename, Saved_response=""):
+        transcription = self.audio_processor.transcribe_audio(audiofile_path, language="en")
+        search_results = self.vectorstore.search_vectorstore(transcription, 5)
+        query = Saved_response + transcription 
+        for i, result in enumerate(search_results, 1):
+            query += f"\n{i}. {result}"
+        try:
+            gemini_response = self.llm_processor.call_gemini_llm("gemini-2.0-flash", query)
+        except Exception as e:
+            print("Gemini error:", e)
+            gemini_response = f"Error: {str(e)}"
+        gemini_response = remove_stars(gemini_response)
+        Saved_response += "transcription: " + transcription + "\n" + "gemini_response: " + gemini_response + "\n"
+        self.audio_processor.log_conversation(transcription, bot_text=gemini_response)
+        audio_data, sample_rate = asyncio.run(self.tts.cpu_stream_to_audio(gemini_response))
+        sf.write(output_filename, audio_data, sample_rate)
+        return Saved_response
+
+    def speech_to_text(self, audiofile_path, Saved_response=""):
+        try:
+            transcription = self.audio_processor.transcribe_audio(audiofile_path, language="en")
+        except Exception as ex:
+            print(f"Transcription error: {ex}")
+            transcription = ""
+        
+        if not transcription:
+            print("No transcription available; aborting further processing.")
+            return "", Saved_response
+
+        try:
+            search_results = self.vectorstore.search_vectorstore(transcription, 5)
+        except Exception as ex:
+            print(f"Vectorstore search error: {ex}")
+            search_results = []
+        
+        query = f"{Saved_response}{transcription}"
+        for i, result in enumerate(search_results, 1):
+            query += f"\n{i}. {result}"
+        
+        try:
+            gemini_response = self.llm_processor.call_gemini_llm("gemini-2.0-flash", query)
+            gemini_response = remove_stars(gemini_response)
+        except Exception as e:
+            print("Gemini error:", e)
+            gemini_response = f"Error: {str(e)}"
+        gemini_response = remove_stars(gemini_response)
+        Saved_response += f"transcription: {transcription}\n" + f"gemini_response: {gemini_response}\n"
+        self.audio_processor.log_conversation(transcription, bot_text=gemini_response)
+        return gemini_response, Saved_response
+
+    def text_to_text(self, message, Saved_response=""):
+        search_results = self.vectorstore.search_vectorstore(message, 5)
+        query = Saved_response + message 
+        for i, result in enumerate(search_results, 1):
+            query += f"\n{i}. {result}"
+        
+        try:
+            gemini_response = self.llm_processor.call_gemini_llm("gemini-2.0-flash", query)
+            gemini_response = remove_stars(gemini_response)
+        except Exception as e:
+            print("Gemini error:", e)
+            gemini_response = f"Error: {str(e)}"
+        Saved_response += "message: " + message + "\n" + "gemini_response: " + gemini_response + "\n"
+        self.audio_processor.log_conversation(message, bot_text=gemini_response)
+        return gemini_response, Saved_response
+
+# Create Flask API instance.
+flask_app = Flask(__name__)
+
+# Instantiate the CancerApp with your vectorstore path.
+vectordb_path = os.path.join("VectorDB", "vectorstore_20250328_011300")
+cancer_app_instance = CancerApp(vectordb_path)
+
+#implement html
+@flask_app.route('/')
+def index():
+    return render_template('index.html')
+
+# Endpoint for text-to-text processing.
+@flask_app.route('/text_to_text', methods=['POST'])
+def api_text_to_text():
+    data = request.get_json()
+    message = data.get("message", "")
+    saved_response = data.get("Saved_response", "")
+    response, saved_response = cancer_app_instance.text_to_text(message, Saved_response=saved_response)
+    return jsonify({"gemini_response": response, "Saved_response": saved_response})
+
+# Endpoint for text-to-speech processing.
+@flask_app.route('/text_to_speech', methods=['POST'])
+def api_text_to_speech():
+    data = request.get_json()
+    message = data.get("message", "")
+    output_filename = data.get("output_filename", "output.wav")
+    saved_response = data.get("Saved_response", "")
+    saved_response = cancer_app_instance.text_to_speech(message, output_filename, Saved_response=saved_response)
+    return jsonify({"output_filename": output_filename, "Saved_response": saved_response})
+
+# Endpoint for speech-to-text processing.
+@flask_app.route('/speech_to_text', methods=['POST'])
+def api_speech_to_text():
+    if 'audiofile' not in request.files:
+        return jsonify({"error": "No audio file provided"}), 400
+    audio_file = request.files['audiofile']
+    # Save uploaded file temporarily.
+    audio_ext = audio_file.filename.split('.')[-1]
+    audio_path = f"temp_audio_input.{audio_ext}"
+    audio_file.save(audio_path)
+    saved_response = request.form.get("Saved_response", "")
+    gemini_response, saved_response = cancer_app_instance.speech_to_text(audio_path, Saved_response=saved_response)
+    os.remove(audio_path)
+    return jsonify({"gemini_response": gemini_response, "Saved_response": saved_response})
+
+# Endpoint for speech-to-speech processing.
+@flask_app.route('/speech_to_speech', methods=['POST'])
+def api_speech_to_speech():
+    if 'audiofile' not in request.files:
+        return jsonify({"error": "No audio file provided"}), 400
+    audio_file = request.files['audiofile']
+    audio_ext = audio_file.filename.split('.')[-1]
+    audio_path = f"temp_audio_input.{audio_ext}"
+    audio_file.save(audio_path)
+    output_filename = request.form.get("output_filename", "speech_output.wav")
+    saved_response = request.form.get("Saved_response", "")
+    saved_response = cancer_app_instance.speech_to_speech(audio_path, output_filename, Saved_response=saved_response)
+    os.remove(audio_path)
+    # Return the output file for download.
+    return send_file(output_filename, as_attachment=True)
+
+if __name__ == '__main__':
+    flask_app.run(host='0.0.0.0', port=7860, debug=True)