update

app.py

@@ -48,116 +48,53 @@ question_embeddings = generate_question_embeddings()
 # Initialize translator
 translator = GoogleTranslator(source="auto", target="en")
 
-# Suggested Questions
-suggested_questions = ["What is AI?", "Tell me a joke!", "How does machine learning work?"]
-
-# Function to extract text from uploaded files
-def extract_text_from_file(uploaded_file):
-    """Extracts text from TXT, PDF, or DOCX files."""
-    file_extension = uploaded_file.name.split(".")[-1].lower()
-    
-    if file_extension == "txt":
-        return uploaded_file.getvalue().decode("utf-8")
-    elif file_extension == "pdf":
-        reader = PdfReader(uploaded_file)
-        return "\n".join([page.extract_text() for page in reader.pages if page.extract_text()])
-    elif file_extension == "docx":
-        doc = docx.Document(uploaded_file)
-        return "\n".join([para.text for para in doc.paragraphs])
-    else:
-        return None
-
-def get_best_response(user_input):
-    """Finds the closest matching dataset question using similarity or generates a response."""
-    input_embedding = embedding_model.encode(user_input, convert_to_tensor=True)
-    similarities = util.pytorch_cos_sim(input_embedding, question_embeddings)[0].cpu()
-    
-    best_match_idx = torch.argmax(similarities).item()
-    best_match_score = similarities[best_match_idx].item()
-    
-    if best_match_score > 0.7:
-        return answers[best_match_idx]
-
-    # Generate response using BlenderBot
-    inputs = chatbot_tokenizer(user_input, return_tensors="pt")
-    outputs = chatbot_model.generate(**inputs)
-    return chatbot_tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-def analyze_sentiment(text):
-    """Analyzes sentiment and returns an emoji representation."""
-    sentiment = TextBlob(text).sentiment.polarity
-    if sentiment > 0:
-        return "😊 Positive"
-    elif sentiment < 0:
-        return "😞 Negative"
-    else:
-        return "😐 Neutral"
-
-def log_chat(user_input, bot_response):
-    """Logs chat to a JSON file."""
-    log_entry = {
-        "timestamp": str(datetime.datetime.now()),
-        "user_input": user_input,
-        "bot_response": bot_response
-    }
-    with open("chat_log.json", "a") as log_file:
-        json.dump(log_entry, log_file)
-        log_file.write("\n")
-
-def text_to_speech(text):
-    """Converts chatbot response to speech and provides download link."""
-    tts = gTTS(text=text, lang="en")
-    audio_file = BytesIO()
-    tts.write_to_fp(audio_file)
-    audio_file.seek(0)
-    return audio_file
+# Video Call Configuration
+RTC_CONFIG = RTCConfiguration({"iceServers": [{"urls": ["stun:stun.l.google.com:19302"]}]})
 
-def generate_chat_pdf():
-    """Creates a PDF of the chat history and returns it as a downloadable file."""
-    buffer = BytesIO()
-    c = canvas.Canvas(buffer, pagesize=letter)
-    width, height = letter
-    
-    y_position = height - 40  # Start at top
-    
-    c.setFont("Helvetica-Bold", 14)
-    c.drawString(30, y_position, "Chat History")
-    y_position -= 20
-    c.setFont("Helvetica", 10)
-    
-    for message in st.session_state.messages:
-        role = "User: " if message["role"] == "user" else "Bot: "
-        text = role + message["content"]
-        
-        for line in text.split("\n"):
-            if y_position < 40:  # New page if reaching bottom
-                c.showPage()
-                c.setFont("Helvetica", 10)
-                y_position = height - 40
-            
-            c.drawString(30, y_position, line)
-            y_position -= 15
-    
-    c.save()
-    buffer.seek(0)
-    return buffer
+# Initialize video call session state
+if "video_call_active" not in st.session_state:
+    st.session_state.video_call_active = False
 
 # Streamlit UI
 st.title("🤖 AI Chatbot with File Upload & Video Calling 🚀")
 
-# File Upload Feature
+# 📹 **Video Call Feature**
+st.subheader("📹 Video Call")
+
+if st.button("📞 Start Video Call"):
+    st.session_state.video_call_active = True
+
+if st.button("❌ End Video Call"):
+    st.session_state.video_call_active = False
+
+if st.session_state.video_call_active:
+    webrtc_streamer(key="video-chat", mode=WebRtcMode.SENDRECV, rtc_configuration=RTC_CONFIG)
+
+# 📝 **File Upload Feature**
 uploaded_file = st.file_uploader("📄 Upload a document for Q&A", type=["txt", "pdf", "docx"])
 
 if uploaded_file:
-    extracted_text = extract_text_from_file(uploaded_file)
+    extracted_text = None
+    file_extension = uploaded_file.name.split(".")[-1].lower()
+
+    if file_extension == "txt":
+        extracted_text = uploaded_file.getvalue().decode("utf-8")
+    elif file_extension == "pdf":
+        reader = PdfReader(uploaded_file)
+        extracted_text = "\n".join([page.extract_text() for page in reader.pages if page.extract_text()])
+    elif file_extension == "docx":
+        doc = docx.Document(uploaded_file)
+        extracted_text = "\n".join([para.text for para in doc.paragraphs])
+
     if extracted_text:
         st.subheader("📜 Extracted File Content:")
         st.text_area("File Content", extracted_text, height=200)
     else:
         st.warning("Unsupported file format.")
 
-# Suggested Questions
+# 💡 **Suggested Questions**
 st.subheader("💡 Suggested Questions:")
+suggested_questions = ["What is AI?", "Tell me a joke!", "How does machine learning work?"]
 cols = st.columns(len(suggested_questions))
 
 user_input = None  
@@ -165,7 +102,7 @@ for i, q in enumerate(suggested_questions):
     if cols[i].button(q):
         user_input = q
 
-# Voice Input
+# 🎤 **Voice Input**
 st.subheader("🎤 Speak instead of typing!")
 if st.button("🎙️ Use Voice Input"):
     recognizer = sr.Recognizer()
@@ -179,15 +116,16 @@ if st.button("🎙️ Use Voice Input"):
     except sr.RequestError:
         user_input = "Speech recognition service error."
 
-# Video Call Feature
-st.subheader("📹 Video Call")
-if st.button("📞 Start Video Call"):
-    webrtc_streamer(key="video-call", mode=WebRtcMode.SENDRECV)
-
+# ✍️ **Text Input**
 if user_input is None:
     user_input = st.chat_input("Type your message here...")
 
-# Initialize chat history
+# 🗑️ **Clear Chat Button**
+if st.button("🗑️ Clear Chat"):
+    st.session_state.messages = []
+    st.rerun()
+
+# 📌 **Chat Processing**
 if "messages" not in st.session_state:
     st.session_state.messages = []
 
@@ -196,19 +134,55 @@ if user_input:
     if translated_text != user_input:
         user_input = translated_text
 
-    response = get_best_response(user_input)
-    log_chat(user_input, response)
+    input_embedding = embedding_model.encode(user_input, convert_to_tensor=True)
+    similarities = util.pytorch_cos_sim(input_embedding, question_embeddings)[0].cpu()
+    best_match_idx = torch.argmax(similarities).item()
+    best_match_score = similarities[best_match_idx].item()
 
-    sentiment_result = analyze_sentiment(user_input)
+    if best_match_score > 0.7:
+        response = answers[best_match_idx]
+    else:
+        inputs = chatbot_tokenizer(user_input, return_tensors="pt")
+        outputs = chatbot_model.generate(**inputs)
+        response = chatbot_tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    sentiment = TextBlob(user_input).sentiment.polarity
+    sentiment_result = "😊 Positive" if sentiment > 0 else "😞 Negative" if sentiment < 0 else "😐 Neutral"
 
     st.session_state.messages.append({"role": "user", "content": user_input})
     st.session_state.messages.append({"role": "assistant", "content": response})
 
-    audio_file = text_to_speech(response)
+    tts = gTTS(text=response, lang="en")
+    audio_file = BytesIO()
+    tts.write_to_fp(audio_file)
+    audio_file.seek(0)
 
     with st.chat_message("assistant"):
         st.write(f"{response}\n\n**Sentiment Analysis:** {sentiment_result}")
         st.audio(audio_file, format="audio/mp3")
 
-    pdf_file = generate_chat_pdf()
-    st.download_button("📥 Download Chat as PDF", pdf_file, "chat_history.pdf", "application/pdf")
+    # 📥 **Download Chat as PDF**
+    buffer = BytesIO()
+    c = canvas.Canvas(buffer, pagesize=letter)
+    width, height = letter
+    y_position = height - 40
+
+    c.setFont("Helvetica-Bold", 14)
+    c.drawString(30, y_position, "Chat History")
+    y_position -= 20
+    c.setFont("Helvetica", 10)
+
+    for message in st.session_state.messages:
+        role = "User: " if message["role"] == "user" else "Bot: "
+        text = role + message["content"]
+        for line in text.split("\n"):
+            if y_position < 40:
+                c.showPage()
+                c.setFont("Helvetica", 10)
+                y_position = height - 40
+            c.drawString(30, y_position, line)
+            y_position -= 15
+
+    c.save()
+    buffer.seek(0)
+    st.download_button("📥 Download Chat as PDF", buffer, "chat_history.pdf", "application/pdf")